In every data analysis, putting the analysis and the results into a comprehensible report is the final, and for some, the biggest hurdle. The goal of a technical report is to communicate information. However, the technical information is difficult to understand because it is complicated and not readily known. Add math anxiety and the all too prevalent notion that anything can be proven with statistics and you can understand why reporting on a data analysis is a challenge.
The ability to write effective reports on a data analysis shouldn’t be assumed. It’s not the same as writing a report for a class project that only the instructor will read. It’s not uncommon for data analysts to receive little or no training in this style of technical writing. Some data analysts have never done it, and they fear the process. Some haven’t done it much, and they think every report is pretty much the same. Some learned under different conditions, like writing company newsletters, and figure they know everything there is to know about it. And worst of all, some have done it without guidance and have developed bad habits, but don’t know it.
It’s a pretty safe bet that if you haven’t taken college classes or professional development courses, haven’t been mentored on the job, and haven’t done some independent reading, you have a bit to learn about writing technical reports. Report writing is like any other skill, you get better by learning more about the process and by practicing. Here are four things you can try to improve your skills.
- Educate yourself. Learn what other people think about technical writing. Visit websites on “statistical analysis reports” and “technical writing,” there are millions of them. Take online or local classes. Read books and manuals. Join Internet groups, such as through Yahoo, Google, or LinkedIn. Immerse yourself in the topic as you did when you were in school.
- Understand criticism. Over the course of your career, you’ll give and receive a lot of criticism on technical reports. Not all criticism is created equal. First, consider the source. Some critics have never written a report on a data analysis and some have never even analyzed data. Still, if the critic is the one paying the bills you have to deal with it. For your part, you should learn how to provide constructivecriticism. Unless a report you are reviewing is a complete mess, respect the report writer’s discretion for structure and format. Focus on content. Be nice.
- Download examples. Search the internet for examples of data analysis reports (Hint: adding pdf and download to the search might help). Critique them. Who’s the audience? What’s the message? What’s good and bad about each report? Which reports do you think are good examples? What do they do that you might want to do yourself in the future?
- Find what’s right for you. When you search the Internet for advice on technical writing or take a few classes from knowledgeable instructors, you’ll hear some different opinions. Everyone will talk about audience and content but most will have more limited views of report organization, writing style, and how you work at writing. Ignore what the experts tell you to do if it doesn’t feel right. Just be sure that the path you eventually choose works for you and the audiences who will read your reports.
If you’ve done all that, it’s just a matter of practice. You’ll learn something from each report you write. If you are new to the process of reporting on a data analysis, consider these six easy lessons:
Lesson 1—Know your content
Lesson 2—Know your audience
Lesson 3—Know your route
Lesson 4—Get their attention
Lesson 5—Get it done
Lesson 6—Get acceptance
Lesson 1—Know your Content
Start with what you know best. In writing a data analysis report, what you know best would be the statistics, graphing, and modeling you did.
You should be able to describe how you characterized the population, how you generated the data or the sources that provided them, what problems you found in the data during your exploratory analysis, how you scrubbed the data, what you did to treat outliers, what transformations you applied, what you did about dropouts and replicates, and what you did with violations of assumptions and non-significant results.
From that, you’ll need to determine what’s important, and then, what’s important to the reader. Unless you’re writing the report to your Professor in college or your peers in a group of professional data analysts, you can be pretty sure that no one will want to hear about all the issues you had to deal with, the techniques you used, or how hard you workedon the analysis. No one will care if your results came from Excel or an R program you wrote. They’ll just want to hear your conclusions. So, what’s the message you want to deliver? That’s the most important thing you’ll have to keep in mind while writing.
Once you work out your message, write an overview to the report so you’ll know where you’re going. It will help you stay on track. Your summary might take one of three forms:
- Executive Summary. Aimed at decision makers and people with not enough time or patience to read more than 400 words. Limit your summary to less than one-page, do not use any jargon, and provide only the result the decision maker needs to know to take an appropriate action (i.e., the message you want to convey).
- Overview. Aimed at most people, whether they would read the report or not. An overview is an abridged version of what is in the report, with a focus on the message you want to convey. The overview shouldn’t be more than a few pages.
- Abstract. Aimed at peers and other people who understand data analysis. An abstract summarizes in a page or less everything of importance that you did, from defining the population through assessing effect sizes. Abstracts are most often used in academic articles.
Once you understand who your audience is, you can rewrite the summary to catch the attention of your readers.
Read more about using statistics at the Stats with Cats blog. Join other fans at the Stats with Cats Facebook group and the Stats with Cats Facebook page. Order Stats with Cats: The Domesticated Guide to Statistics, Models, Graphs, and Other Breeds of Data Analysis at Wheatmark, amazon.com, barnesandnoble.com, or other online booksellers.
Originally appeared on Stats with Cats.