how to write a statistical report example

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How to Write a Statistical Report

Last Updated: March 6, 2024 Fact Checked

This article was reviewed by Grace Imson, MA and by wikiHow staff writer, Jennifer Mueller, JD . Grace Imson is a math teacher with over 40 years of teaching experience. Grace is currently a math instructor at the City College of San Francisco and was previously in the Math Department at Saint Louis University. She has taught math at the elementary, middle, high school, and college levels. She has an MA in Education, specializing in Administration and Supervision from Saint Louis University. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 404,226 times.

A statistical report informs readers about a particular subject or project. You can write a successful statistical report by formatting your report properly and including all the necessary information your readers need. [1] X Research source

A Beginner’s Guide to Statistical Report Writing

Use other statistical reports as a guide to format your own. Type your report in an easy-to-read font, include all the information that your reader needs, and present your results in a table or graph.

Formatting Your Report

• If you're completing your report for a class, your instructor or professor may be willing to show you some reports submitted by previous students if you ask.
• University libraries also have copies of statistical reports created by students and faculty researchers on file. Ask the research librarian to help you locate one in your field of study.
• You also may be able to find statistical reports online that were created for business or marketing research, as well as those filed for government agencies.
• Be careful following samples exactly, particularly if they were completed for research in another field. Different fields of study have their own conventions regarding how a statistical report should look and what it should contain. For example, a statistical report by a mathematician may look incredibly different than one created by a market researcher for a retail business.

• You typically want to have 1-inch margins around all sides of your report. Be careful when adding visual elements such as charts and graphs to your report, and make sure they don't bleed over the margins or your report may not print properly and will look sloppy.
• You may want to have a 1.5-inch margin on the left-hand side of the page if you anticipate putting your study into a folder or binder, so all the words can be read comfortably when the pages are turned.
• Don't double-space your report unless you're writing it for a class assignment and the instructor or professor specifically tells you to do so.
• Use headers to add the page number to every page. You may also want to add your last name or the title of the study along with the page number.

• Citation methods typically are included in style manuals, which not only detail how you should cite your references but also have rules on acceptable punctuation and abbreviations, headings, and the general formatting of your report.
• For example, if you're writing a statistical report based on a psychological study, you typically must use the style manual published by the American Psychological Association (APA).
• Your citation method is all the more important if you anticipate your statistical report will be published in a particular trade or professional journal.

• If you're creating your statistical report for a class, a cover sheet may be required. Check with your instructor or professor or look on your assignment sheet to find out whether a cover sheet is required and what should be included on it.
• For longer statistical reports, you may also want to include a table of contents. You won't be able to format this until after you've finished the report, but it will list each section of your report and the page on which that section starts.

• If you decide to create section headings, they should be bold-faced and set off in such a way that they stand out from the rest of the text. For example, you may want to center bold-faced headings and use a slightly larger font size.
• Make sure a section heading doesn't fall at the bottom of the page. You should have at least a few lines of text, if not a full paragraph, below each section heading before the page break.

• Check the margins around visual elements and make sure the text lines up and is not too close to the visual element. You want it to be clear where the text ends and the words associated with the visual element (such as the axis labels for a graph) begin.
• Visual elements can cause your text to shift, so you'll need to double-check your section headings after your report is complete and make sure none of them are at the bottom of a page.
• Where possible, you also want to change your page breaks to eliminate situations in which the last line of a page is the first line of a paragraph, or the first line of a page is the last line of a paragraph. These are difficult to read.

Creating Your Content

• Avoid overly scientific or statistical language in your abstract as much as possible. Your abstract should be understandable to a larger audience than those who will be reading the entire report.
• It can help to think of your abstract as an elevator pitch. If you were in an elevator with someone and they asked you what your project was about, your abstract is what you would say to that person to describe your project.
• Even though your abstract appears first in your report, it's often easier to write it last, after you've completed the entire report.

• Aim for clear and concise language to set the tone for your report. Put your project in layperson's terms rather than using overly statistical language, regardless of the target audience of your report.
• If your report is based on a series of scientific experiments or data drawn from polls or demographic data, state your hypothesis or expectations going into the project.
• If other work has been done in the field regarding the same subject or similar questions, it's also appropriate to include a brief review of that work after your introduction. Explain why your work is different or what you hope to add to the existing body of work through your research.

• Include a description of any particular methods you used to track results, particularly if your experiments or studies were longer-term or observational in nature.
• If you had to make any adjustments during the development of the project, identify those adjustments and explain what required you to make them.
• List any software, resources, or other materials you used in the course of your research. If you used any textbook material, a reference is sufficient – there's no need to summarize that material in your report.

• Start with your main results, then include subsidiary results or interesting facts or trends you discovered.
• Generally you want to stay away from reporting results that have nothing to do with your original expectations or hypotheses. However, if you discovered something startling and unexpected through your research, you may want to at least mention it.
• This typically will be the longest section of your report, with the most detailed statistics. It also will be the driest and most difficult section for your readers to get through, especially if they are not statisticians.
• Small graphs or charts often show your results more clearly than you can write them in text.

• When you get to this section of your report, leave the heavy, statistical language behind. This section should be easy for anyone to understand, even if they skipped over your results section.
• If any additional research or study is necessary to further explore your hypotheses or answer questions that arose in the context of your project, describe that as well.

• It is often the case that you see things in hindsight that would have made data-gathering easier or more efficient. This is the place to discuss those. Since the scientific method is designed so that others can repeat your study, you want to pass on to future researchers your insights.
• Any speculation you have, or additional questions that came to mind over the course of your study, also are appropriate here. Just make sure you keep it to a minimum – you don't want your personal opinions and speculation to overtake the project itself.

• For example, if you compared your study to a similar study conducted in another city the year before yours, you would want to include a citation to that report in your references.
• Cite your references using the appropriate citation method for your discipline or field of study.
• Avoid citing any references that you did not mention in your report. For example, you may have done some background reading in preparation for your project. However, if you didn't end up directly citing any of those sources in your report, there's no need to list them in your references.

• Avoid trade "terms of art" or industry jargon if your report will be read mainly by people outside your particular industry.
• Make sure the terms of art and statistical terms that you do use in your report are used correctly. For example, you shouldn't use the word "average" in a statistical report because people often use that word to refer to different measures. Instead, use "mean," "median," or "mode" – whichever is correct.

Presenting Your Data

• This is particularly important if you're submitting your report for publication in a trade journal. If the pages are different sizes than the paper you print your report on, your visual elements won't line up the same way in the journal as they do in your manuscript.
• This also can be a factor if your report will be published online, since different display sizes can cause visual elements to display differently.
• The easiest way to label your visual elements is "Figure," followed by a number. Then you simply number each element sequentially in the order in which they appear in your report.
• Your title describes the information presented by the visual element. For example, if you've created a bar graph that shows the test scores of students on the chemistry class final, you might title it "Chemistry Final Test Scores, Fall 2016."

• Make sure each visual element is large enough in size that your readers can see everything they need to see without squinting. If you have to shrink down a graph to the point that readers can't make out the labels, it won't be very helpful to them.
• Create your visual elements using a format that you can easily import into your word-processing file. Importing using some graphics formats can distort the image or result in extremely low resolution.

• For example, if you have hundreds of samples, your x axis will be cluttered if you display each sample individually as a bar. However, you can move the measure on the y axis to the x axis, and use the y axis to measure the frequency.
• When your data include percentages, only go out to fractions of a percentage if your research demands it. If the smallest difference between your subjects is two percentage points, there's no need to display more than the whole percentage. However, if the difference between your subjects comes down to hundredths of a percent, you would need to display percentages to two decimal places so the graph would show the difference.
• For example, if your report includes a bar graph of the distribution of test scores for a chemistry class, and those scores are 97.56, 97.52, 97.46, and 97.61, your x axis would be each of the students and your y axis would start at 97 and go up to 98. This would highlight the differences in the students' scores.

• Be careful that your appendix does not overwhelm your report. You don't necessarily want to include every data sheet or other document you created over the course of your project.
• Rather, you only want to include documents that reasonably expand and lead to a further understanding of your report.
• For example, when describing your methods you state that a survey was conducted of students in a chemistry class to determine how they studied for the final exam. You might include a copy of the questions the students were asked in an appendix. However, you wouldn't necessarily need to include a copy of each student's answers to those questions.

Statistical Report Outline

Community Q&A

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• ↑ https://www.ibm.com/docs/en/iotdm/11.3?topic=SSMLQ4_11.3.0/com.ibm.nex.optimd.dg.doc/11arcperf/oparcuse-r-statistical_reports.html
• ↑ https://www.examples.com/business/report/statistics-report.html
• ↑ https://collaboratory.ucr.edu/sites/g/files/rcwecm2761/files/2019-04/Final_Report_dan.pdf
• ↑ https://tex.stackexchange.com/questions/49386/what-is-the-recommended-font-to-use-for-a-statistical-table-in-an-academic-journ
• ↑ https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/citing-references.html
• ↑ https://www.youtube.com/watch?v=kl3JOCmuil4

About This Article

Start your statistical report with an introduction explaining the purpose of your research. Then, dive into your research methods, how you collected data, and the experiments you conducted. Present you results with any necessary charts and graphs, but do not discuss or analyze the numbers -- in a statistical report, all analysis should happen in the conclusion. Once you’ve finished writing your report, draft a 200 word abstract and create a cover sheet with your name, the date, and the report title. Don’t forget to cite the appropriate references when necessary! For more formatting help, read on! Did this summary help you? Yes No

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Applied Statistics

11. Statistical report writing

Learning to write useful, productive and readable statistical reports is a critical data analysis skill.

• Undergraduate Statistics Project
• Statistical Report Writing
• The book  R eport Writing for Data Science in R by Roger D. Peng (freely dowloaded from LeanPub) is a useful reference for statistical report writing, especially when using the R programming language.
• The book C ommunicating with Data by Deborah Nolan and Sara Stoudt consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities

One model for a self-contained statistical report, especially one containing empirical methods, has the following sections:

• Give an informative title to your project.
• Assessment : Does the title give an accurate preview of what the report is about? Is it informative, specific and precise?
• The abstract provides a brief summary of the paper (background, methods, results and conclusions). The suggested length is no more than 150 words.  This allows you approximately 1 sentence (and likely no more than 2 sentences) summarizing each of the following sections.  Typically, abstracts are the last thing you write.
• Assessment : Are the main points of the report described clearly and succinctly?
• In this section you are providing the background of the report – the topic for analysis –  and arguing its significance.  Well-accepted facts or referenced statements should serve as the majority of content of this section.  Typically, the background and significance section starts very broad and moves towards the specific area of analysis.
• Assessment :

Does the background and significance have a logical organization? Does it move from the general to the specific?

Has sufficient background been provided to understand the report?  

Does this section end with statements about the goals of the report?

• Data collection .  Explain how the data was collected/experiment was conducted.
• Variable creation . Detail the variables in your analysis and how they are defined (if necessary).  For example, if you created a combined (frequency times quantity) drinking variable you should describe how.  If you are talking about gender no further explanation is really needed.
• Analytic Methods .  Explain the statistical procedures that will be used to analyze your data.  E.g. Boxplots are used to illustrate differences in GPA across gender and class standing.  Correlations are used to assess the impacts of gender and class standing on GPA.
• Assessment : Could the study be repeated based on the information given here? Is the material organized into logical categories (like those above)?
• Typically, results sections start with descriptive statistics, e.g. what percent of the sample is male/female, what is the mean GPA overall, in the different groups, etc. Figures can be nice to illustrate these differences! However, information presented must be relevant in helping to answer the research question(s) of interest.  Typically, inferential (i.e. hypothesis tests) statistics come next.  Tables can often be helpful for results from multiple regression. Do not give computer output here! This should look like a peer-reviewed journal article results section.  Tables and figures should be labeled, embedded in the text, and referenced appropriately.  The results section typically makes for fairly dry reading.  It does not explain the impact of findings, it merely highlights and reports statistical information.

Is the content appropriate for a results section?  Is there a clear description of the results?

Are the results/data analyzed well?  Given the data in each figure/table is the interpretation accurate and logical?  Is the analysis of the data thorough? Is anything  relevant ignored?

Are the figures/tables appropriate for the data being discussed?  Are the figure legends and titles clear and concise?

• Restate your objective and draw connections between your analyses and objective.  In other words, how did (or didn’t) you answer/address your objective.  Place these all in the larger scope of the relevant chapter of the text.  Talk about the limitations of your findings and possible areas for future research to better investigate your research question.  End with a concluding sentence or two that summarizes your key findings and impact on the field.

Do you clearly state whether the results answer any question posed?

Were specific data cited from the results to support each interpretation?  Do you clearly articulate the basis for supporting or rejecting any hypotheses?

• You can copy & paste different style references from Google Scholar: here’s an example (click on the “Cite” link to see the different style citations).
• Assessment : Are references appropriate and of adequate quality?  Are the cited properly (both in the text and at the end of the paper)?

Writing quality

• Is your report well-organized, with paragraphs organized in a logical manner?
• Is each paragraph well-written, with a clear topic sentence, and single major point?
• Is your report generally well-written, with good use of language, and sentence structure?
• Are tables and figures labeled correctly and referenced accordingly?
• Does the entire report  flow and answer any question(s) sufficiently?
• Is there extraneous information presented (if so, delete it)?

Advice on writing statistical reports

Andrew Gelman

Andrew Gelman , Professor of Statistics and Political Science and Director of the Applied Statistics Center at Columbia University, wrote the following as a guide to preparing statistical research articles; it works equally well for writing statistical reports.

Please try to follow it, and see how your planning for writing for, and actual writing of, statistical reports improves:

• Start writing the conclusions (the final part of your report, before the references). Write up to a couple pages on what you’ve found and what you recommend. In writing these conclusions, you should also be writing some of the introduction, in that you’ll need to give enough background so that general readers can understand what you’re talking about and why they should care. But you want to start with the conclusions, because that will determine what sort of background information you’ll need to give.
• Now step back. What is the principal evidence for your conclusions? Make some graphs and pull out some key numbers that represent your research findings which back up your claims.
• Back one more step, now. What are the methods and data you used to obtain your findings?
• Now go back and write the literature review and the introduction .
• Moving forward one last time: go to your results and conclusions and give alternative explanations . Why might you be wrong? What are the limits of applicability of your findings? What future work would be appropriate to follow up on these loose ends?
• Write the abstract . An easy way to start is to take the first sentence from each of the first five paragraphs of the article. This probably won’t be quite right, but I bet it will be close to what you need.
• Give the article to a friend, ask them to spend 15 minutes looking at it, then ask what they think your message was, and what evidence you have for it. Your friend should read the article as a potential consumer, not as a critic. You can find typos on your own time, but you need somebody else’s eyes to get a sense of the message you’re sending.

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.

• PLOS Biology
• PLOS Climate
• PLOS Complex Systems
• PLOS Computational Biology
• PLOS Digital Health
• PLOS Genetics
• PLOS Global Public Health
• PLOS Medicine
• PLOS Mental Health
• PLOS Neglected Tropical Diseases
• PLOS Pathogens
• PLOS Sustainability and Transformation
• PLOS Collections
• How to Report Statistics

Ensure appropriateness and rigor, avoid flexibility and above all never manipulate results

In many fields, a statistical analysis forms the heart of both the methods and results sections of a manuscript. Learn how to report statistical analyses, and what other context is important for publication success and future reproducibility.

A matter of principle

First and foremost, the statistical methods employed in research must always be:

Appropriate for the study design

Rigorously reported in sufficient detail for others to reproduce the analysis

Free of manipulation, selective reporting, or other forms of “spin”

Just as importantly, statistical practices must never be manipulated or misused . Misrepresenting data, selectively reporting results or searching for patterns  that can be presented as statistically significant, in an attempt to yield a conclusion that is believed to be more worthy of attention or publication is a serious ethical violation. Although it may seem harmless, using statistics to “spin” results can prevent publication, undermine a published study, or lead to investigation and retraction.

Supporting public trust in science through transparency and consistency

Along with clear methods and transparent study design, the appropriate use of statistical methods and analyses impacts editorial evaluation and readers’ understanding and trust in science.

In 2011  False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant exposed that “flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates” and demonstrated “how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis”.

Arguably, such problems with flexible analysis lead to the “ reproducibility crisis ” that we read about today. 

A constant principle of rigorous science The appropriate, rigorous, and transparent use of statistics is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail. Trust in science is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information and reporting.

Sound statistical practices

While it is hard to provide statistical guidelines that are relevant for all disciplines, types of research, and all analytical techniques,  adherence to rigorous and appropriate principles remains key. Here are some ways to ensure your statistics are sound.

Define your analytical methodology before you begin Take the time to consider and develop a thorough study design that defines your line of inquiry, what you plan to do, what data you will collect, and how you will analyze it. (If you applied for research grants or ethical approval, you probably already have a plan in hand!) Refer back to your study design at key moments in the research process, and above all, stick to it.

To avoid flexibility and improve the odds of acceptance, preregister your study design with a journal Many journals offer the option to submit a study design for peer review before research begins through a practice known as preregistration. If the editors approve your study design, you’ll receive a provisional acceptance for a future research article reporting the results. Preregistering is a great way to head off any intentional or unintentional flexibility in analysis.  By declaring your analytical approach in advance you’ll increase the credibility and reproducibility of your results and help address publication bias, too. Getting peer review feedback on your study design and analysis plan before it has begun (when you can still make changes!) makes your research even stronger AND increases your chances of publication—even if the results are negative or null. Never underestimate how much you can help increase the public’s trust in science by planning your research in this way.

Imagine replicating or extending your own work, years in the future Imagine that you are describing your approach to statistical analysis for your future self, in exactly the same way as we have described for writing your methods section . What would you need to know to replicate or extend your own work? When you consider that you might be at a different institution, working with different colleagues,  using different programs, applications, resources — or maybe even adopting new statistical techniques that have emerged — you can help yourself imagine the level of reporting specificity that you yourself would require to redo or extend your work. Consider:

• Which details would you need to be reminded of? 
• What did you do to the raw data before analysis?
• Did the purpose of the analysis change before or during the experiments?
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal techniques or constraints than to conceal them, which could result in reproducibility or ethical issues downstream.

Existing standards, checklists, guidelines for specific disciplines

You can apply the Open Science practices outlined above no matter what your area of expertise—but in many cases, you may still need more detailed guidance specific to your own field. Many  disciplines, fields, and projects have worked hard to develop guidelines and resources  to help with statistics, and to identify and avoid bad statistical practices. Below, you’ll find some of the key materials. 

TIP: Do you have a specific journal in mind?

Be sure to read the submission guidelines for the specific journal you are submitting to, in order to discover any journal- or field-specific policies, initiatives or tools to utilize.

Articles on statistical methods and reporting

Makin, T.R.,  Orban de Xivry, J. Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript . eLife 2019;8:e48175 (2019).  https://doi.org/10.7554/eLife.48175  

Munafò, M., Nosek, B., Bishop, D. et al. A manifesto for reproducible science . Nat Hum Behav 1, 0021 (2017). https://doi.org/10.1038/s41562-016-0021    

Writing tips

Your use of statistics should be rigorous, appropriate, and uncompromising in avoidance of analytical flexibility. While this is difficult, do not compromise on rigorous standards for credibility!

• Remember that trust in science is everyone’s responsibility.
• Keep in mind future replicability.
• Consider preregistering your analysis plan to have it (i) reviewed before results are collected to check problems before they occur and (ii) to avoid any analytical flexibility.
• Follow principles, but also checklists and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not simple adhering to journal guidelines.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.
• Consider a course in advanced and new statistics, if you feel you have not focused on it enough during your research training.

Don’t

• Misuse statistics to influence significance or other interpretations of results
• Conduct your statistical analyses if you are unsure of what you are doing—seek feedback (e.g. via preregistration) from a statistical specialist first.
• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Write Discussions and Conclusions
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

How to Write Data Analysis Reports in 9 Easy Steps

Table of contents

Enjoy reading this blog post written by our experts or partners.

If you want to see what Databox can do for you, click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

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A reliable source of information

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• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

• Start with an Outline
• Make a Selection of Vital KPIs
• Pick the Right Charts for Appealing Design
• Use a Narrative
• Organize the Information
• Include a Summary
• Careful with Your Recommendations
• Double-Check Everything
• Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

• Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

• Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.

Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.

Is your online store improving or failing? You can easily collect relevant data about your store and monitor the most important metrics like total sales, orders placed, and new customers by downloading this WooCommerce Shop Overview dashboard template .

Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.

Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.

Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .

Rely on Accurate Data with Databox

“I don’t have time to build custom reports from scratch.”

“It takes too long and becomes daunting very soon.”

“I’m not sure how to organize the data to make it effective and prove the value of my work.”

Does this sound like you?

Well, it’s something we all said at some point – creating data analytics reports can be time-consuming and tiring. And you’re still not sure if the report is compelling and understandable enough when you’re done.

That’s why we decided to create Databox dashboards – a world-class solution for saving your money and time. We build streamlined and easy-to-follow dashboards that include all the metrics that you may need and allow you to create custom ones if necessary. That way, you can use templates and adjust them to any new project or client without having to build a report from scratch.

You can skip the setup and get your first dashboard for free in just 24 hours, with our fantastic customer support team on the line to assist you with the metrics you should track and the structure you should use.

Enjoy crafting brilliant data analysis reports that will improve your business – it’s never been faster and more effortless. Sign up today and get your free dashboard in no time.

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At Databox, we’re obsessed with helping companies more easily monitor, analyze, and report their results. Whether it’s the resources we put into building and maintaining integrations with 100+ popular marketing tools, enabling customizability of charts, dashboards, and reports, or building functionality to make analysis, benchmarking, and forecasting easier, we’re constantly trying to find ways to help our customers save time and deliver better results.

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Stefana Zarić is a freelance writer & content marketer. Other than writing for SaaS and fintech clients, she educates future writers who want to build a career in marketing. When not working, Stefana loves to read books, play with her kid, travel, and dance.

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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Writing with Descriptive Statistics

Welcome to the Purdue OWL

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Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.

The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.

Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).

Some fields prefer to put means and standard deviations in parentheses like this:

If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.

If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.

At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.

• How to Write a Statistical Report?
• How to Write a Statistical Report and Make This Process a Doddle

How to Write a Statistical Report: Prologue

The process of writing a statistical data analysis report example, how to do a statistical analysis report: the role of formatting, example of statistical analysis report mistakes: don’t be fooled, where can i get another good statistical analysis report example.

While Luxembourgian economy is relatively small with the total GDP estimating around $58 billion as of 2015, it is characterized by a very high level of incomes and living standards.

Effective communication is a key to success at any modern work environment. The opening phrase of the article is an example of the Statistics class homework writing assignment. Would you like to learn how to write a statistical report? It is important to develop adequate statistical skills supported by the knowledge of the subject, reading, research, and solid writing skills. The post includes valuable tips on how to do a statistical analysis report of a winner.

Statistics is a complex subject. Make your way through by purchasing cheap homework solutions online from the web’s top experienced academic writers!  Say ' I need help with homework ' and get top-notch assistance. It is time to learn how to do a statistical analysis report.

Do not hurry up to understand how to write a statistics report – learn what the term means. Everyone knows Statistics is a complex academic discipline, which involves a plenty of calculations and interpretations of large data sets. It is a scientific term. Read more about statistics in different aspects of life here.

We will start explaining how to write a statistical report with the structure and organization of this type of academic/business assignment.

A 200-word abstract is a perfect way to start many academic papers. This piece of writing contains a summary of the entire text, highlighted major points, target audience, and goals of the project. From this point, move smoothly to the introduction with the clear explanation of why the writer has chosen the specific topic.

The body of such project is different from other academic assignments. The writer must list and describe the chosen research methods and findings based on the obtained data. Why can it be harder? It takes more time & efforts than several body paragraphs with the corresponding number of arguments along with the supportive evidence. The last stage is the same in any type of academic writing: a conclusion.

The examples of topics to let the students realize how to write a statistical report are given in the article:

• Norway is the most prosperous economies in Europe based on research
• A vaccine ABC to fight cancer effectively requires more time – data collected by one of the most influential American Healthcare Organizations
• The recent invention in the field of biotechnology is not effective
• Time-tested tools to fix problems with slow Windows – comparison of the outcomes with the analogical software running on Mac OS X
• The impact of depressions on the children aged 14-16

These papers require statistics and facts.

There is no way to understand how to do a statistical analysis report without recognizing the importance of correct formatting. Do it in the next section!

How to do a statistical analysis report regarding formatting? In most cases, the students face the formatting approaches listed below:

It all depends on the area of study. While Psychology teachers prefer assigning essays to be written in APA paper format , Sociology tutors recommend using ASA style . The students can read writing style manuals available online.

If the student were not involved in statistical report writing before, he/she would benefit from having a look at the valuable online examples of different similar papers to use as the good templates. Without this information, the student won’t learn how to write a statistical report properly. Ask the teacher about the format in case he forgets to five one.

In addition to online databases of papers, the students may attend school or college library, which contains at least one great example left from the previous educational years. Smart students share their works to support newer generations.

Mind that you have no right to copy the information from the selected example: plagiarism results in F grade.

The most recommended format is shown below.

Make single-inch margins around the sides of the page; watch out when attaching components like charts, tables, and graphs to the project. Leave a 1.5-inch margin on the left-hand side. Do it in case you plan to put the project into a folder/binder. Unlike other homework writing assignments (essay, research paper ), this type of task does not require a double-spaced format.

Do not forget about the headers! They should specify the number of pages, brief version of the title, author’s name, and some other details reminding the person what he/she is reading.

The last thing left to do to learn how to do a statistical analysis report is to analyze the most common mistakes Statistics students make.

We have discussed how to write a statistical analysis report of A-level; never forget to check the finished papers to detect possible mistakes. Those could be small, insignificant typos, which will not influence the final grade; those could be serious failures (grammar, word choice, etc.) Read the finished paper to find the following issues:

• Incomplete or incorrect punctuation
• Grammar errors
• Spelling mistakes
• Incorrect font
• Wrong paragraph/line breaks
• Missing words/phrases
• Incorrect amounts in tables & graphs

Is there nothing to fix? The absence of technical mistakes does not mean the author cannot find a better way to express certain things. Leave your final draft away. Come back to the essay in a few days to read it with a fresh look and rested the eye; fix any parts you believe sound ridiculous or can be improved.

Writing statistical reports is a time-consuming process, which requires in-depth knowledge of the studied subject, writing & research skills, ability to assume and analyze things competitively.

Congratulations – now you know how to do a statistical analysis report, it's goals, formatting, and the mistakes to avoid during the process. Need more information? To learn how to write a statistical report in more details, grab some good examples online. Students who are more interested in obtaining professional help instead of learning how to write a statistical report should remember about the opportunity to order a custom research paper from the professional essayists at our website!

School and college teachers often ask students to use the Harvard outline format (known as date-and-author referencing) to write their academic assignments. This type of your instructor’s request may shock you because you understand neither standard Harvard outline format rules or guidelines nor APA...

Nowadays scholars, professors and academics regard ASA (or American Sociological Association) format as the chief method of citation, along with APA or MLA styles. ASA citation format causes difficulties to students, in spite of the fact that there is nothing hard about it.This format, as well as AS...

The first sign of getting older in terms of education is a new writing assignment known as a research paper. Compared to a regular high school or college homework  - writing an essay, college research paper requires much more time and effort. For example - you don’t have to add an abstract and appen...

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• Knowledge Base

The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organisations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organise and summarise the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalise your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarise your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, frequently asked questions about statistics.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalise your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalisable findings, you should use a probability sampling method. Random selection reduces sampling bias and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to be biased, they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalising your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalise your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialised, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalised in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardised indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarise them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organising data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualising the relationship between two variables using a scatter plot .

By visualising your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimise the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasises null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

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Other students also liked, a quick guide to experimental design | 5 steps & examples, controlled experiments | methods & examples of control, between-subjects design | examples, pros & cons, more interesting articles.

• Central Limit Theorem | Formula, Definition & Examples
• Central Tendency | Understanding the Mean, Median & Mode
• Correlation Coefficient | Types, Formulas & Examples
• Descriptive Statistics | Definitions, Types, Examples
• How to Calculate Standard Deviation (Guide) | Calculator & Examples
• How to Calculate Variance | Calculator, Analysis & Examples
• How to Find Degrees of Freedom | Definition & Formula
• How to Find Interquartile Range (IQR) | Calculator & Examples
• How to Find Outliers | Meaning, Formula & Examples
• How to Find the Geometric Mean | Calculator & Formula
• How to Find the Mean | Definition, Examples & Calculator
• How to Find the Median | Definition, Examples & Calculator
• How to Find the Range of a Data Set | Calculator & Formula
• Inferential Statistics | An Easy Introduction & Examples
• Levels of measurement: Nominal, ordinal, interval, ratio
• Missing Data | Types, Explanation, & Imputation
• Normal Distribution | Examples, Formulas, & Uses
• Null and Alternative Hypotheses | Definitions & Examples
• Poisson Distributions | Definition, Formula & Examples
• Skewness | Definition, Examples & Formula
• T-Distribution | What It Is and How To Use It (With Examples)
• The Standard Normal Distribution | Calculator, Examples & Uses
• Type I & Type II Errors | Differences, Examples, Visualizations
• Understanding Confidence Intervals | Easy Examples & Formulas
• Variability | Calculating Range, IQR, Variance, Standard Deviation
• What is Effect Size and Why Does It Matter? (Examples)
• What Is Interval Data? | Examples & Definition
• What Is Nominal Data? | Examples & Definition
• What Is Ordinal Data? | Examples & Definition
• What Is Ratio Data? | Examples & Definition
• What Is the Mode in Statistics? | Definition, Examples & Calculator

Statistics Made Easy

The Complete Guide: How to Report ANOVA Results

A one-way ANOVA is used to determine whether or not there is a statistically significant difference between the means of three or more independent groups.

When reporting the results of a one-way ANOVA, we always use the following general structure:

• A brief description of the independent and dependent variable.
• The overall F-value of the ANOVA and the corresponding p-value.
• The results of the post-hoc comparisons (if the p-value was statistically significant).

Here’s the exact wording we can use:

A one-way ANOVA was performed to compare the effect of [independent variable] on [dependent variable].   A one-way ANOVA revealed that there [was or was not] a statistically significant difference in [dependent variable] between at least two groups (F(between groups df, within groups df) = [F-value], p = [p-value]).   Tukey’s HSD Test for multiple comparisons found that the mean value of [dependent variable] was significantly different between [group name] and [group name] (p = [p-value], 95% C.I. = [lower, upper]).   There was no statistically significant difference between [group name] and [group name] (p=[p-value]).

The following example shows how to report the results of a one-way ANOVA in practice.

Example: Reporting the Results of a One-Way ANOVA

Suppose a researcher recruits 30 students to participate in a study. The students are randomly assigned to use one of three studying techniques for the next month to prepare for an exam. At the end of the month, all of the students take the same test.

The researcher then performs a one-way ANOVA to determine if there is a difference in mean exam scores between the three groups.

The following table shows the results of the one-way ANOVA along with the Tukey post-hoc multiple comparisons table:

Here is how to report the results of the one-way ANOVA:

A one-way ANOVA was performed to compare the effect of three different studying techniques on exam scores.   A one-way ANOVA revealed that there was a statistically significant difference in mean exam score between at least two groups (F(2, 27) = [4.545], p = 0.02).   Tukey’s HSD Test for multiple comparisons found that the mean value of exam score was significantly different between technique 1 and technique 2 (p = 0.024, 95% C.I. = [-14.48, -0.92]).   There was no statistically significant difference in mean exam scores between technique 1 and technique 3 (p=0.883) or between technique 2 and technique 3 (p=0.067).

Things to Keep in Mind

Here are a few things to keep in mind when reporting the results of a one-way ANOVA:

Use a descriptive statistics table.

It can be helpful to present a descriptive statistics table that shows the mean and standard deviation of values in each treatment group as well to give the reader a more complete picture of the data.

For example, SPSS produces the following descriptive statistics table that shows the mean and standard deviation of exam scores for students in each of the three study technique groups:

Only report post-hoc results if necessary.

If the overall p-value of the ANOVA is not statistically significant, then you will not conduct post-hoc multiple comparisons between groups. This means you obviously don’t have to report any post-hoc results in the final report.

If you do have to conduct post-hoc tests, the Tukey HSD test is the most commonly used one but occasionally you may use the Scheffe or Bonferroni test instead.

Round p-values when necessary.

As a general rule of thumb, the overall F value and any p-values in ANOVA results are rounded to either two or three decimal places for brevity.

No matter how many decimal places you choose to use, be sure to be consistent throughout the report.

Additional Resources

The following tutorials explain how to report other statistical tests and procedures in APA format:

How to Report Two-Way ANOVA Results (With Examples) How to Report Cronbach’s Alpha (With Examples) How to Report t-Test Results (With Examples) How to Report Chi-Square Results (With Examples) How to Report Pearson’s Correlation (With Examples) How to Report Regression Results (With Examples)

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

12 Replies to “The Complete Guide: How to Report ANOVA Results”

very informative

Your content is very detailed and yet simple to follow. Thanks a lot Zach for all the great work!

Hi. Can you help me in reporting more than three groups? Does it come the same way as this.

Thank you, for the explanation on the ANOVA and Post Host results writing

I find this platform very educative and would like to learn more on how to interpret more statistical information under you. Thanks

This was extremely helpful! Thank you!

Guide me on how t- can be used in comparison of variables

Thank you! Bless your soul. 🙂

I gained some knowledge

zach thank you for the guide to report anova results , you are a legend.

If the results of ANOVA contradict that of the post-hoc. ANOVA is significant but the post hoc is not, how do I report the results?????

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A Statistical Report: How to Write It Easily?

Communicative skills have a crucial role in modern society, and there are plenty of areas where you need to master them. One of such areas is a statistical report. In can be useful in both educational and professional purposes. How to write it properly? The answer to this question will be complex. To develop your statistical report writing skills you need to have a decent level of subject insight, reading, and research as well as writing skills, of course. In our article, you will learn some useful recommendations on how to become a master of statistical analysis report writing.

You also have an opportunity to skip the whole learning process by ordering your homework done by our academic writers who are experts in any possible academic field to help you. But we will share some writing tips with you anyway.

Before any special writing task, you need to make sure you understand the term correctly. It is a well-known truth that Statistics is a very complex subject that requires skills in calculations and data interpretation. You need to know how to explain the numbers you receive during calculations. This is real science. And there are plenty of areas where statistics allows a deeper understanding of the things.

We will discuss the process of writing statistical reports starting with its structure and organization of this academic or business writing task.

Examples of How to Write a Statistical Data Analysis Report

The best way to start your paper is to write an abstract with 200 words. This part of the paper will include the basic info the whole paper, pointing out main points, goals and the target readers. Then you should move gradually to the introductory part where you need to explain why you have chosen this particular topic.

The structure of the body will have some unique features comparing to other writing assignments. In this part, you should specify what research methods you have chosen and why as well as mention what finding did you get after analyzing the received data. This can be challenging as you need to more efforts and time to arrange the paragraphs and arguments along the text. Then you need to provide a logical conclusion.

Here are some examples of topics that can be used for writing statistical reports:

• Researches that prove that Norway is on the top place in Europe considering prosperity.
• We need more time to develop the anti-cancer vaccine – data, provided by one of the respectable Healthcare Organization in the USA.
• The newest biotechnological invention is not effective.
• What tools allow fixing the problem of the slow Windows performance in comparison with similar tools for Mac OS X.
• The effect of depression on kids 13-17 years old.

To write such papers, you will need to use facts and statistical data.

One more thing you need to realize after the statistical report term is the importance of the proper formatting. So the next chapter will be about this aspect.

The Importance of Formatting of a Statistical Analysis Report

Let’s take a closer look at the formatting issue. Usually, students have a list of the following formatting styles:

The choice usually depends on the study area. For instance, the teachers of Psychology require using APA format while writing an essay on their subject, while Sociology teachers prefer ASA. All the manuals on the required styles can be easily found online.

In case you did not write a statistical report before, we advise you to find some good examples of various papers of this type and use them as samples to develop your own paper eventually. In fact, students have to use various templates and examples to learn how to create a decent paper, especially when it comes to the statistical report writing. Just pay attention to the format you need and the one you should use for your paper.

Apart from online sources of information, you can get benefits from the college or university libraries. As a rule, they store numerous papers on various topics and styles that were approved in particular educational institutions. This is the way the best students provide help to the next generations.

But you need to realize that those examples are only for educational purposes and you cannot simply copy the data from them, because it will be considered as plagiarism.

Below you can find out what format is used more often and what its specifications are.

Make one-inch spaces from each side of the page. From the left side leave 1.5-inch space in case your paper will be placed into the folder with other works. Check the sizes of attached charts, tables and graphs as well. You do not have to use double-spacing in this type of academic paper though.

Pay attention to the header of each page. Include to it the number of the page, short version of the title, the name of the writer and other details that will reflect the material the person reads.

As soon as we now know how to format your paper correctly, let’s take a look at some typical mistakes that students make while working on their statistical report projects.

Typical Mistakes You Should Avoid While working on Your Statistical Report

You know how to make you a statistical report paper worth the best grade. But the most important thing is to check the paper when it is done. This will help you to detect some mistakes that could spoil the impression of the paper and fix them when you still can. Remember, even the minor issues could have a serious impact on the general “reputation” of your paper. Check your report on the following issues:

• punctuation mistakes;
• improper grammar and spelling;
• wrong font/paragraph marks/lines/etc.;
• missing words or phrases;
• a wrong number of attachments.

Does your paper contain any of these mistakes? Even if you know for sure that your paper is technically correct, you still need to revise it in order to check whether you have used the proper approaches and provide all the required explanations. As soon as you get the final draft, leave it for a few days if you have time and get back to it later. This method will help you to take a fresh look at your work.

Remember, that you are working on the writing task that is of the academic level, so you need to use all your skills and knowledge and improve them if needed to provide the best result.

Sources of the Examples of the Statistical Analysis Reports

After reading this article properly, you have a general understanding of what your report should look like considering content and the formatting. You can enhance your insight into academic writing by using available online sources of examples and guides. You also can save time and order the original statistical report on your topic. This may help you to master your skills in your next writing assignments.

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Statistics for Data Science: A Complete Guide

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Data science is all about finding meaning in data, and statistics is the key to unlocking those insights. Consider statistics as the vocabulary that data scientists employ to comprehend and analyze data. Without it, data is just a jumble of numbers.

A strong background in statistics is essential for anyone hoping to work as a data scientist. It’s the tool that empowers you to turn raw data into actionable intelligence, make informed decisions, and drive real-world impact. In this guide, we’ll break down the key concepts, tools, and applications of statistics in data science, providing you with the knowledge you need to succeed in this exciting field.

Fundamentals of Statistics

Statistics provides the framework for understanding and interpreting data. It enables us to calculate uncertainty, spot trends, and draw conclusions about populations from samples. In data science, a strong grasp of statistical concepts is crucial for making informed decisions, validating findings, and building robust models.

1. Descriptive Statistics

Descriptive statistics help us summarize and describe the key characteristics of a dataset. This includes measures of central tendency like mean (average), median (middle value), and mode (most frequent value), which tell us about the typical or central value of a dataset. We also use measures of variability, such as range (difference between maximum and minimum values), variance , and standard deviation , to understand how spread out the data is. Additionally, data visualization techniques like histograms, bar charts, and scatter plots provide visual representations of data distributions and relationships, making it easier to grasp complex patterns.

2. Inferential Statistics

Inferential statistics, on the other hand, allow us to make generalizations about a population based on a sample. This involves understanding how to select representative samples and how they relate to the overall population. Hypothesis testing is a key tool in inferential statistics, allowing us to evaluate whether a hypothesis about a population is likely to be true based on sample data. We also use confidence intervals to estimate the range of values within which a population parameter is likely to fall. Finally, p-values and significance levels help us determine the statistical significance of results and whether they are likely due to chance.

Why Does Statistics Matter in Data Science?

Statistics is the foundation of the entire field of data science, not just a theoretical subject found in textbooks. It’s the engine that drives data-driven decision-making, allowing you to extract meaningful insights, test hypotheses, and build reliable models.

Applications of Statistics in Data Science Projects:

Statistics is an integral part of data science projects and finds numerous applications at each stage of such projects, from data exploration to model building and validation. Here’s how:

• Data Collection: Designing surveys or experiments to gather representative samples that accurately reflect the target population.
• Data Cleaning: Identifying and handling outliers, missing values, and anomalies using statistical techniques.
• Exploratory Data Analysis (EDA): Summarizing data, visualizing distributions, and identifying relationships between variables using descriptive statistics and graphs.
• Feature engineering: Selecting and transforming variables to improve model performance, often based on statistical insights.
• Model Building: Using statistical models like linear regression, logistic regression, or decision trees to make predictions or classify data.
• Model Evaluation: Assessing the accuracy and reliability of models using statistical metrics like R-squared, precision, recall, and F1 score.
• Hypothesis Testing: Formulating and testing hypotheses about relationships between variables to draw valid conclusions.
• A/B Testing: Comparing the performance of different versions of a product or website to determine which one is more effective, using statistical significance tests.

Examples of Statistical Methods in Real-world Data Analysis:

Here are some examples of how statistical methods are applied in real-world data analysis:

• Healthcare: Statistical methods can be used for analyzing clinical trial data to determine the effectiveness of a new drug or treatment.
• Finance: Building risk models to assess the creditworthiness of borrowers.
• Marketing: Identifying customer segments and predicting their buying behaviour.
• E-commerce: Personalizing product recommendations based on customer preferences.
• Manufacturing: Optimizing production processes to reduce defects and improve efficiency.

By applying statistical methods, data scientists can uncover hidden patterns in data, make accurate predictions, and drive data-driven decision-making across various domains. Whether it’s predicting customer churn, optimizing pricing strategies, or detecting fraudulent activity, statistics play a pivotal role in transforming raw data into actionable insights.

The Fundamental Statistics Concepts for Data Science

Statistics provides the foundation for extracting meaningful insights from data. Understanding these key concepts will empower you to analyze data effectively, build robust models, and make informed decisions in the field of data science.

1. Correlation

Correlation quantifies the relationship between two variables. The correlation coefficient, a value between -1 and 1, indicates the strength and direction of this relationship. A positive correlation means that as one variable increases, so does the other, while a negative correlation means that as one variable increases, the other decreases. Pearson correlation measures linear relationships, while Spearman correlation assesses monotonic relationships.

2. Regression

Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Linear regression models a linear relationship, while multiple regression allows for multiple independent variables. Logistic regression is used when the dependent variable is categorical, such as predicting whether a customer will churn or not.

Bias refers to systematic errors in data collection, analysis, or interpretation that can lead to inaccurate conclusions. Selection, measurement, and confirmation bias are examples of different types of bias. Mitigating bias requires careful data collection and analysis practices, such as random sampling, blinding, and robust statistical methods.

4. Probability

Probability is the study of random events and their likelihood of occurrence. Expected values, variance, and probability distributions are examples of fundamental probability concepts. Conditional probability and Bayes’ theorem allow us to update our beliefs about an event based on new information.

5. Statistical Analysis

Statistical analysis is the process of testing hypotheses and making inferences about data using statistical techniques. Analysis of variance (ANOVA) compares means between multiple groups, while chi-square tests assess the relationship between categorical variables.

6. Normal Distribution

Numerous natural phenomena can be described by the normal distribution, commonly referred to as the bell curve. It is a common probability distribution. It’s characterized by its mean and standard deviation. Z-scores standardize values relative to the mean and standard deviation, allowing us to compare values from different normal distributions.

By mastering these fundamental statistical concepts, you will be able to analyze data, identify patterns, make predictions, and draw meaningful conclusions that will aid in data science decision-making. 

Statistics in Relation To Machine Learning

While machine learning frequently takes center stage in data science, statistics is its unsung hero. Statistical concepts underpin the entire machine learning process, from model development and training to evaluation and validation. Understanding this connection is essential for aspiring data scientists and anyone seeking to harness the power of machine learning.

The Role of Statistics in Machine Learning:

Statistics and machine learning are closely intertwined disciplines. Here’s how they relate:

• Model Development: Machine learning models are created and designed using statistical methods such as regression and probability distributions. These models are essentially mathematical representations of relationships within data.
• Training and Optimization: Statistical optimization techniques, such as gradient descent, are used to fine-tune the parameters of machine learning models, enabling them to learn from data and make accurate predictions.
• Model Evaluation: Statistical metrics like accuracy, precision, recall, and F1 score are used to assess the performance of machine learning models. These metrics help data scientists select the best-performing model and identify areas for improvement.
• Hypothesis Testing: Statistical hypothesis testing determines whether the observed results of a machine learning model are statistically significant or simply random.
• Data Preprocessing: Statistical techniques like normalization and standardization are applied to prepare data for machine learning algorithms.

Examples of Statistical Techniques Used in Machine Learning:

Certainly, many statistical techniques form the backbone of machine learning algorithms. Here are a few examples:

• Linear Regression: A statistical model used for predicting a continuous outcome variable based on one or more predictor variables.
• Logistic Regression: A statistical model used for predicting a binary outcome (e.g., yes/no, true/false) based on one or more predictor variables.
• Bayesian Statistics: A probabilistic framework that combines prior knowledge with observed data to make inferences and predictions.
• Hypothesis Testing: A statistical method for evaluating whether a hypothesis about a population is likely to be true based on sample data.
• Cross-Validation: A technique for assessing how well a machine learning model will generalize to new, unseen data.

Statistical Software Used in Data Science

Data scientists have access to a vast collection of statistical software, each with its own set of strengths and capabilities. Whether you’re just starting your data science journey or you’re a seasoned professional, familiarizing yourself with these tools is essential for efficient and effective data analysis.

• Excel: While often overlooked, Excel remains a powerful tool for basic data analysis and visualization. Its user-friendly interface and built-in functions make it accessible for beginners, while its flexibility allows for custom calculations and data manipulation.
• R: It is a statistical programming language specifically designed for data analysis and visualization. It boasts a vast collection of packages and libraries for various statistical techniques, making it a favorite among statisticians and data analysts. 
• Python: Known for its versatility and ease of use, Python has become the go-to language for data science. It offers a rich ecosystem of libraries like NumPy (for numerical operations), pandas (for data manipulation and analysis), SciPy (for scientific computing), and stats models (for statistical modeling), making it a powerful tool for data scientists.
• MySQL: It is a popular open-source relational database management system (RDBMS), is widely used to store and manage structured data. Its ability to handle large datasets and perform complex queries makes it essential for data scientists working with relational data.
• SAS: It is a comprehensive statistical analysis software suite used in various industries for tasks like business intelligence, advanced analytics, and predictive modeling. It offers a wide range of statistical procedures, data management tools, and reporting capabilities.
• Jupyter Notebook: A web-based interactive computing environment that allows data scientists to create and share documents that combine code, visualizations, and narrative text. It’s a popular tool for data exploration, prototyping, and collaboration.

The software used is frequently determined by the task at hand, the type of data, and personal preferences. Many data scientists use a combination of these tools to leverage their strengths and tackle diverse challenges.

Practical Applications and Case Studies

Statistics isn’t just theoretical; it’s the engine powering many of the most impactful data science applications across industries. Here are a few examples where statistical methods play a pivotal role:

1. Customer Churn Prediction (Telecommunications):

A telecommunications company was experiencing a high rate of customer churn, losing valuable revenue. Data scientists tackled this problem by building a logistic regression model using historical customer data. This model analyzed various factors, including call patterns, data usage, customer service interactions, and billing history, to predict the likelihood of each customer churning. Armed with these predictions, the company could proactively reach out to high-risk customers with personalized retention offers and tailored services, ultimately reducing churn and improving customer loyalty.

2. Fraud Detection (Finance):

A financial institution was losing millions of dollars annually due to fraudulent transactions. To combat this, data scientists implemented anomaly detection algorithms based on statistical distributions and probability theory. These algorithms continuously monitored transaction data, flagging unusual patterns or outliers that could indicate fraudulent activity. This allowed the institution to investigate and block potentially fraudulent transactions in real time, significantly reducing financial losses.

3. Disease Prediction (Healthcare):

In the realm of healthcare, data scientists are using survival analysis and predictive modeling techniques to predict the risk of diseases like diabetes and heart disease. By analyzing patient data, including demographics, medical history, lifestyle factors, and genetic information, these models can identify high-risk individuals. Armed with this knowledge, healthcare providers can offer personalized preventive care and early interventions, potentially saving lives and improving overall health outcomes.

4. Recommender Systems (e-commerce):

E-commerce giants like Amazon and Netflix rely heavily on recommender systems to drive customer engagement and sales. These systems use collaborative filtering and matrix factorization, statistical techniques that analyze vast amounts of user behavior and product/content data. By understanding user preferences and item characteristics, recommender systems can suggest products or movies that are most likely to resonate with each individual, resulting in personalized shopping experiences and increased revenue.

These case studies demonstrate how statistics enables data scientists to tackle complex problems, uncover hidden patterns, and provide actionable insights that drive business value across industries. By leveraging statistical methods, you can create innovative solutions that have a real-world impact, from improving customer satisfaction to saving lives.

Read More Article:

• Data Science Roadmap
• How to Become a Data Scientist
• Career Transition to Data Science
• Data Science Career Opportunities
• Best Data Science Courses Online

Statistics is the foundation on which data science is built. It provides the essential tools for understanding, analyzing, and interpreting data, allowing us to uncover hidden patterns, make informed decisions, and drive innovation.

From the fundamental concepts of descriptive and inferential statistics to the advanced techniques used in machine learning, statistics empowers data scientists to transform raw data into actionable insights. By mastering the concepts discussed in this guide, you’ll be well-equipped to tackle the challenges of data analysis, build robust models, and make data-driven decisions that have a real-world impact. Remember, statistics is not just a subject to be studied; it’s a powerful tool that can unlock the full potential of data and propel your career in data science to new heights.

If you’re ready to dive deeper into the world of data science, consider exploring Scaler’s comprehensive Data Science Course . They offer a well-structured curriculum, expert instruction, and career support to help you launch your career in this exciting field.

What statistics are needed for data science?

Data science requires a solid foundation in descriptive and inferential statistics, including measures of central tendency and variability, probability distributions, hypothesis testing, regression analysis, and sampling techniques.

What are the branches of statistics?

The two primary branches of statistics are descriptive statistics, which summarize and describe data, and inferential statistics, which draw conclusions about populations from samples. Other branches include Bayesian statistics, non-parametric statistics, and robust statistics.

What is the importance of statistics in data science?

Statistics is important in data science because it provides tools for analyzing and interpreting data, developing reliable models, making informed decisions, and effectively communicating findings. It’s the backbone of the entire data science process, from data collection to model evaluation.

Can I learn statistics for data science online?

Yes, numerous online courses and resources are available to learn statistics for data science. Platforms such as Coursera, edX, and Udemy provide courses ranging from beginner to advanced levels, which are frequently taught by experienced professionals and academics.

How do I apply statistical concepts in data science projects?

Statistical concepts are used throughout the data science workflow. You can use descriptive statistics to summarize data, inferential statistics to test hypotheses, regression analysis to predict outcomes, and various other techniques depending on the specific project and its goals.

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• The Scientist University

How to Write a Good Results Section

Effective results sections need to be much more than a list of data points given without context. .

Nathan Ni holds a PhD from Queens University. He is a science editor for The Scientist’s Creative Services Team who strives to better understand and communicate the relationships between health and disease.

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The results section details the findings of a given study. The primary difference between the results section and the discussion section is that the results section does not delve into hypothetical interpretation. However, people are often taught in school that a results section should only present data and include nothing else. This goes too far—a results section that is only a list of numbers and facts is confusing, boring, and difficult to read. When presenting their results, authors need to exercise discretion and nuance. Most importantly, they need to provide context for their numbers and comparative reference points for their data.

Why Did the Authors Want This Data?

Before jumping right into the dataset, authors should explain the rationale behind why they chose to generate the dataset. While there is no need to overly rehash the introduction, the reader still benefits from a brief primer on what the authors sought to examine through this particular experimentation and the resulting data.

Here are some examples of what this means in practice. Look at the following passage:

“In order to test the plausibility of this model, we implement a Brownian dynamics simulation based on prior modeling of meiotic chromosome movement and pairing.” 1  

The authors use the first clause—“In order to test the plausibility of this model”—to explain why the second clause—“we implement a Brownian dynamics simulation”—took place. 

Similarly, consider another example : 

“MRGPRX4 engages intracellular G q to induce calcium flux. Using calcium imaging as a readout, we screened 3808 drugs for activity against human embryonic kidney (HEK) 293 cells expressing MRGPRX4 (the Ser83, rs2445179 variant).” 2  

Here, the first sentence clearly sets up why the authors employed calcium imaging to study drug activity against HEK293 cells.

Why Did the Authors Choose These Parameters?

In addition to why they chose to perform a certain experiment, it is also important for scientists to tell their audience why they examined selected specific parameters or variables in their experiments. Too often, authors will highlight or emphasize numbers in a sentence without contextualizing them. Based on the syntax, the reader recognizes that these numbers are significant, but does not immediately understand why. 

Biologist Gary T. ZeRuth from Murray State University, in a recent article in Islets , provides an example of how to contextualize experimental parameters and results:

“Given that INS1 cells are normally maintained in 11.1 mM glucose, expression of  Ins2, MafA , and  Glis3  was measured in INS1 cells cultured in 3 mM glucose (low glucose), 11.1 mM glucose, and 25 mM glucose (high glucose). Graded levels of expression were observed with expression at 11.1 mM glucose being more similar to low glucose conditions than chronically elevated glucose for all three genes.” 3  

Here, ZeRuth and his colleagues annotate the three parameters—3mM, 11.1mM, and 25mM glucose—as low, normal, and high concentrations. The authors then present their results within this framework: Gene expression at 11.1mM was more similar to that found at low glucose concentrations than high ones. In this way, they show the effect of high glucose versus low glucose and examine the validity of 11.1mM as a baseline. 

What Is the Right Level of Detail for the Data? 

It is important that data is not just dumped en masse onto the reader, but presented in a curated and meaningful way. To do this, researchers have to decide on an appropriate level of detail that provides sufficient evidence and is not overwhelming. In the prior example, ZeRuth and his colleagues did not provide gene expression as an empirical value, but rather as a relative one. In this circumstance, it was more important to emphasize gene expression changes in difficult glucose experiments than to say that gene A expression was 2.3 in high glucose and 1.2 in low glucose. 3  

One good way of determining the right level of detail is to keep the figures in mind when writing the results section. Many times, authors will use the text only as a vehicle to introduce the figures. However, the proper way is actually the opposite, where the figures provide additional depth and detail for the text. It is important that the text is able to stand alone from a narrative and argumentation perspective, while the figures present information that does not translate well to text format, such as high volumes of numbers, multi-parameter comparisons, and more complex statistical analyses.

As an example, consider the following passage:

“Several phosphomonoester compounds including fospropofol {EC 50 : 3.78 nM [95% confidence interval (CI): 1.82 to 6.78]}, fosphenytoin [an antiepileptic drug, EC 50 : 77.01 nM (95% CI: 52.63 to 115.10)], and dexamethasone phosphate [steroid-derived phosphate, EC 50 : 14.68 nM (95% CI: 5.44 to 22.10)] showed high agonist potencies for MRGPRX4 (Fig. 1, C and D, and table S1).” 2

The core statement in this sentence is: “Several phosphomonoester compounds including fospropofol, fosphenytoin, and dexamethasone phosphate showed high agonist potencies for MRGPRX4.” The specific EC 50 values are provided as immediate direct evidence for this claim, as well as for reference, while the figure is referenced only at the end, almost as a “if more information is needed, look here” prompt.

Applying Principles Throughout the Whole Results Section

These considerations should be applied on both a micro level, when presenting the results of each discrete experiment, and on a macro level, across the results section as a whole. Each paragraph should offer a transition to the next. Each presented piece of data should likewise offer some insights as to why the researchers sought the next piece of data. Finally, all of the data together must form a cohesive body that serves as evidence for the interpretations that readers will find in the discussion.

In their work, ZeRuth and his colleagues conclude most paragraphs in the results section with a summary statement that begins with “these data suggest/indicate”. 3 Readers who collate these statements together are rewarded with a de facto abstract for the results section, giving them an accessible and digestible primer on what the authors believe their data shows. 

Looking for more information on scientific writing? Check out  The Scientist’ s   TS SciComm  section. Looking for some help putting together a manuscript, a figure, a poster, or anything else?  The Scientist ’s   Scientific Services  may have the professional help that you need.

• Marshall WF, Fung JC. Modeling homologous chromosome recognition via nonspecific interactions . PNAS . 2024;121(20):e2317373121.
• Chien DC, et al. MRGPRX4 mediates phospho-drug-associated pruritus in a humanized mouse model . Sci Transl Med . 2024;16(746):eadk8198. 
• Grieve LM, et al. Downregulation of Glis3 in INS1 cells exposed to chronically elevated glucose contributes to glucotoxicity-associated β cell dysfunction . Islets . 2024;16(1):2344622.

How to write a scientific report at university

David foster, professor in science and engineering at the university of manchester, explains the best way to write a successful scientific report.

David H Foster

At university, you might need to write scientific reports for laboratory experiments, computing and theoretical projects, and literature-based studies – and some eventually as research dissertations. All have a similar structure modelled on scientific journal articles. Their special format helps readers to navigate, understand and make comparisons across the research field.

Scientific report structure

The main components are similar for many subject areas, though some sections might be optional.

If you can choose a title, make it informative and not more than around 12 words. This is the average for scientific articles. Make every word count.  

The abstract summarises your report’s content in a restricted word limit. It might be read separately from your full report, so it should contain a micro-report, without references or personalisation.  

Usual elements you can include:  

• Some background to the research area.
• Reason for the work.
• Main results.
• Any implications.

Ensure you omit empty statements such as “results are discussed”, as they usually are.  

Introduction  

The introduction should give enough background for readers to assess your work without consulting previous publications.  

It can be organised along these lines:  

• An opening statement to set the context.  
• A summary of relevant published research.
• Your research question, hypothesis or other motivation.  
• The purpose of your work.
• An indication of methodology.
• Your outcome.

Choose citations to any previous research carefully. They should reflect priority and importance, not necessarily recency. Your choices signal your grasp of the field.  

Literature review  

Dissertations and literature-based studies demand a more comprehensive review of published research than is summarised in the introduction. Fortunately, you don’t need to examine thousands of articles. Just proceed systematically.  

• Use two to three published reviews to familiarise yourself with the field.  
• Use authoritative databases such as Scopus or Web of Science to find the most frequently cited articles.  
• Read these articles, noting key points. Experiment with their order and then turn them into sentences, in your own words.  
• Get advice about expected review length and database usage from your individual programme.

Aims and objectives  

Although the introduction describes the purpose of your work, dissertations might require something more accountable, with distinct aims and objectives.

The aim or aims represent the overall goal (for example, to land people on the moon). The objectives are the individual tasks that together achieve this goal (build rocket, recruit volunteers, launch rocket and so on).

The method section must give enough detail for a competent researcher to repeat your work. Technical descriptions should be accessible, so use generic names for equipment with proprietary names in parentheses (model, year, manufacturer, for example). Ensure that essential steps are clear, especially any affecting your conclusions.

The results section should contain mainly data and analysis. Start with a sentence or two to orient your reader. For numeric data, use graphs over tables and try to make graphs self-explanatory. Leave any interpretations for the discussion section.

The purpose of the discussion is to say what your results mean. Useful items to include:  

• A reminder of the reason for the work.
• A review of the results. Ensure you are not repeating the results themselves; this should be more about your thoughts on them.
• The relationship between your results and the original objective.
• Their relationship to the literature, with citations.  
• Any limitations of your results.  
• Any knowledge you gained, new questions or longer-term implications.

The last item might form a concluding paragraph or be placed in a separate conclusion section. If your report is an internal document, ensure you only refer to your future research plans.  

Try to finish with a “take-home” message complementing the opening of your introduction. For example: “This analysis has shown the process is feasible, but cost will decide its acceptability.”  

Five common mistakes to avoid when writing your doctoral dissertation   9 tips to improve your academic writing Five resources to help students with academic writing

Acknowledgements  

If appropriate, thank colleagues for advice, reading your report and technical support. Make sure that you secure their agreement first. Thank any funding agency. Avoid emotional declarations that you might later regret. That is all that is required in this section.  

Referencing  

Giving references ensures other authors’ ideas, procedures, results and inferences are credited. Use Web of Science or Scopus as mentioned earlier. Avoid databases giving online sources without journal publication details because they might be unreliable.

Don’t refer to Wikipedia. It isn’t a citable source.  

Use one referencing style consistently and make sure it matches the required style of your degree or department. Choose either numbers or author and year to refer to the full references listed near the end of your report. Include all publication details, not just website links. Every reference should be cited in the text.  

Figures and tables  

Each figure should have a caption below with a label, such as “Fig. 1”, with a title and a sentence or two about what it shows. Similarly for tables, except that the title appears above. Every figure and table should be cited in the text.

Theoretical studies  

More flexibility is possible with theoretical reports, but extra care is needed with logical development and mathematical presentation. An introduction and discussion are still needed, and possibly a literature review.

Final steps

Check that your report satisfies the formatting requirements of your department or degree programme. Check for grammatical errors, misspellings, informal language, punctuation, typos and repetition or omission.

Ask fellow students to read your report critically. Then rewrite it. Put it aside for a few days and read it afresh, making any new edits you’ve noticed. Keep up this process until you are happy with the final report. 

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Academic Report

Report generator.

As a way of evaluating a student’s logical capacity, comprehension level and writing skill , some professors require their students to write a document presenting their ideas, thoughts, analyses, etc. about a certain topic. Other than writing an essay , the students can also use a report in order effectively present their objective deductions and findings.

A formal report is another way of presenting facts and analysis you have gathered from your readings about a certain topic. In requires thorough research, readings, rationalizing, analyzing and making a point. It goes beyond that of an essay, it is more than just arguing a position and drawing conclusions, although a report can also do that, it must comprehensively present pertinent facts and information in order for the reader to see the subject in new light.

As you may know, report writing is a very useful skill not only academically but also in your future career. Not only does it hones your writing skills it also improves your analytical and critical thinking skills since it urges you to come up with objective findings based on facts. Therefore, it will surely help you be good at whatever job you wish to pursue in the future; no employer says no to a critically and analytically adept individual. You may also see marketing report examples.

Academic Research Report Template

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Academic Report Format Guide Example

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Difference Between an Essay and Report

An essay and a report are both effective ways of presenting information and data. However, some professors may prefer one over the other. In order to know the difference between the two, a list of their differences are presented below:

• Essay are rarely used outside the academic realm.
• It focuses on analyzing or evaluating theory, past research by other people, and ideas.
• Rarely presents the findings of a newly conducted research.
• It only has four significant parts or elements.
• The flow of writing is continuous and does not have dividing sections.
• It usually does not include table, charts, and/or diagrams.
• It should not be used as the method in arriving at conclusions.
• Is usually not reflective about the process of researching and writing the essay itself.
• It does not include recommendations.
• It is argumentative and mostly based on ideas.
• Only offers conclusions on a question or on presented issues or problems.

You may also see business report examples.

• Originated from the professional world but is still used academically.
• Often presents data and findings that the researcher himself has gathered.
• Uses data gathering methods such as surveys, experiment or case study, or by applying theory.
• Commonly has at least 12 parts or sections and 14 parts or sections at most.
• Topics are divided into different sections or headings or sub-headings.
• It usually contains tables, graphs, charts and diagrams.
• Includes the method/s the researcher used.
• It includes recommendations on what actions to make.
• It is an informative and fact-based document.
• Follows specific style for each section.
• It is written with a specific purpose and reader in mind.

You may also like examples of short report .

Management Decisions and Control Academic Report Example

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Digital Storytelling Academic Report Example

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Flood Mitigation and Water Storage Engineering Academic Report Example

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Contents of an Academic Report

An effective academic report must have the contents and sections necessary to nit-pick and through explain a subject. Listed below are the contents of an academic report:

• Author Declaration
• Abstract or Executive Summary
• Acknowledgements
• Table of Contents
• Introduction
• Literature Review
• Method or Methodology or Research Design
• Results or Findings
• Discussion of Results or Analysis or Interpretation
• Conclusions
• Recommendations
• References or Bibliography

How to Write an Academic Report

1. title page.

This means what it literally means. The title of the general report should be indicated on this page of the academic report. In some cases, the title page also includes your name as the author and student number, the name of the course and the course code. For example:

Communication Skills Relevant in International Business

John Smith (012345) Business 300

2. Author Declaration

In some universities or colleges, you will need to fill out a form from the department or faculty conforming that the report is in fact your own output. This form is attached to any assigned report or essay for your course.

3. Abstract or Executive Summary

An abstract is a short opening for your entire report. It is a basically a summary of the report as a whole and therefore should only be around 150 words in length. In order to effectively write it, a good techniques is writing it after all the sections, headings and sub-headings have been presented. Here’s a tip: write one or two sentences representing each section of the report in order to have a complete and comprehensive abstract.

4. Acknowledgements

Although acknowledgements are normally necessary in major reports, it can also be included in an academic report. This acknowledges the people who have supported you in your research and has contributed in the completion of the report. However, do not go overboard. This should only be short and direct to the point. You may also like consulting report examples.

6. Table of Contents

This is where the reader goes to look for specific sections or topics found in your report. This contains the actual titles of each section, heading and sub-headings along with their actual page numbers. A good way or organizing your table of contents is to list the contents in according to hierarchy numbers, from first to last. After the list of the contents comes a separate list for the tables, charts, diagrams, etc that is found in your report. You may also check out management report examples.

7. Introduction

The introduction must present the purpose or objective of the report and explain why the report is necessary or how it’s useful. It must immediately let the reader know that the report is useful in the field it is focused on and that it has a positive impact and recommendations on the subject at hand. In addition, you can define key terms you have repeatedly used in the report so that the reader has a clear idea on what you mean when you use the term. You might be interested in recruitment report examples.

Author’s Note : The following sections (8-11) are primarily used in major reports such as research, an experiment, survey or observation. If your report is based on reading, you can replace these sections with topic heading of your own choosing.

8. Literature Review

In this section, describe and report the previous and current thinking and research on the topic. You include a summary on what other have written about the topic you are reporting. This section will mostly consist of in-text citations from the books, articles, reports, etc. you have read about the topic. You may also see report examples in excel .

Simply, it is a review of all the literature you have read in order to form your own thinking about the topic. These literature are your basis for conducting your own report. The literature review should follow the format, MLA or APA format, you professor has required in citing your references.

9. Method or Methodology or Research Design

This section is all about the method or way you have gathered or collected your data. You present and tell your reader/s how were you able the data you have in your report. For example, you can describe the step-by-step process you did when you conducted an experiment or write a detailed description of a situation you have observed. In addition, in this section it is normal that you also have to explain why you collected the data through that method. An normally, the justification should also be quite detailed. You can include some in-text references to research methods references to help explain and justify your choice of method(s). You may also like monthly report examples & samples.

10. Results or Findings

Simply present the results or findings of your report in this section. There is no need for discussions, analysis and explanations of the results. Oftentimes, this section includes a table to comprehensively present the findings. Aside from that, this is also where you state whether you accept or reject the hypothesis or hypotheses you have made in you report. You may also check out sample activity reports .

11. Discussion of Results or Analysis or Interpretation

This is where you present what you think about the results you have formulated in your report. You can also include comment abut your results in this section. Here are other things the discussion section can include:

• Describing and suggesting reasons for any patterns in the results, possibly including anomalies (results that don’t ‘fit in with’ the rest).
• Explaining what you found (perhaps with reference to theory). You may also see performance report examples.
• Commenting on how much your findings agree or disagree with the literature.
• Considering the accuracy and reliability of your results (and how the methods you used might have affected that accuracy).
• Considering the implications of your results – what they might mean for your practice, for example. • Discussing what further research in this area might be useful in future. You may also like investigation report samples and examples.

12. Conclusions

In the conclusions, you should summarize the key findings of your report. Remember that all the information that you include in the conclusions should have been presented before and are new information. The conclusions should effectively summarize and present all the major points you have made so far in you report.

13. Recommendations

Recommendations are not necessarily needed in all academic reports, however, work-related and case studies should always present recommendations. These suggestions are for future actions in order to solve or improve issues or problems presented in the report. You may also check out free report examples & samples.

14. References or Bibliography

There should be a list on all the references you have used to cite and to back your claims. It should only contain all the literature you have cited in your report. Depending on the requirement, you can follow either an MLA or APA format for citation.

15. Appendices

Appendices contains all the supplementary information is ‘stored’. This could be table of data, copies of observation forms or notes, extracts from large documents, a transcript of a recording, etc. You might be interested in technical report examples & samples.

School Program Report Example

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New School Report Example

We hope you found our article on creating an academic report to be useful for your academic studies. We also included some examples which you can use as a reference/guide.

Text prompt

• Instructive
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Generate a report on the impact of technology in the classroom on student learning outcomes

Prepare a report analyzing the trends in student participation in sports and arts programs over the last five years at your school.

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