24  Genre Conventions

Statisticians spend a lot of time writing about their analyses and results so that bosses, clients, coworkers, or students can read and understand their work. For many statisticians, being able to clearly write and explain their work is just as important as being able to do the work. But every genre of writing—statistical report, romance novel, reddit post, history exam essay, literary criticism, or news article—has its own conventions and styles. You may be a good writer for another genre, but statistical writing is different, and you shouldn’t write a statistical report like you’d write an essay for your high school English class.

In this chapter we will review some of the basic features of common styles in statistical writing. This is not a writing course, though (and you should consider taking a writing course!). For a more comprehensive reference, consider Nolan and Stoudt (2021).

24.1 IMRaD structure

IMRaD is a common organization for statistical reports (and research reports in other fields). IMRaD stands for Introduction, Methods, Results, and Discussion, plus perhaps an abstract or executive summary. It became a common style for scientific papers in the 1940s, and by 1980, almost every medical journal article followed the format (Sollaci and Pereira 2004). By now, many articles using data to answer scientific questions, in many fields, follow the IMRaD format either exactly or with slight modifications.

Popularity is not, in itself, a reason to follow the format. But it has two great advantages:

  • As you learn how to write, having a defined format will make it easier for you to organize your ideas in a way that is coherent and logical.
  • Following a common format means your readers will know what to expect in each section and will know where to look to find things.

Let’s examine each section in turn.

24.1.1 The abstract and executive summary

In writing for academic audiences, the convention is for articles to begin with a short abstract that summarizes the paper and its conclusions. To understand what should go in an abstract, it may be helpful to consider how they were used for much of the 1900s. Before scientific papers could easily be downloaded from the Internet, when finding a paper you are interested in required calling libraries and requesting a photocopy be mailed to you, there were abstracting services. These compiled the abstracts of papers published in a particular field and mailed them to subscribers periodically. Subscribers could read through the abstracts, find papers of interest to them, and then go about finding or requesting a copy of the full article.

Abstracts, then, are meant to stand alone. They summarize what the article is about and what conclusions it reaches so the reader can decide if the paper is relevant to them. The abstract should not contain acronyms only defined later in the report, mathematical formulas, figures, or tables, and it should be limited to a few paragraphs at most. Many academic journals limit abstracts to 250 or 500 words. Because abstracts are often the first thing a reader will see—and will determine whether they bother to read the rest of your work—it is important to ensure they are clear and interesting.

For example, here is an abstract from the journal Psychological Science, reporting on experiments studying an important topic:

The current work estimated the relative importance of joke and audience characteristics for the occurrence of amusement. Much psychological research has focused on stimulus characteristics when searching for sources of funniness. Some researchers have instead highlighted the importance of perceiver characteristics, such as dispositional cheerfulness. Across five preregistered studies (Ns = 118–54,905) with varied stimuli and perceiver samples (website visitors, students, Mechanical Turk and Prolific users), variance-decomposition analyses found that perceiver characteristics account for more variance in funniness ratings than stimulus characteristics. Thus, psychological theories focusing on between-persons differences have a relatively high potential for explaining and predicting humor appreciation (here, funniness ratings). Crucially, perceiver-by-stimulus interactions explained the largest amount of variance, highlighting the importance of fit between joke and audience characteristics when predicting amusement. Implications for humor-appreciation theories and applications are discussed. (Rosenbusch, Evans, and Zeelenberg 2022)

Notice how this abstract sets up the problem to be solved, its context in relation to previous psychological research, the studies that were conducted, a summary of the results, and how those results help resolve the problem. This is exactly what other researchers in the field would want to know, concisely described in just one paragraph. Without any of those individual components, the abstract would be much less useful to those readers.

Some journals require structured abstracts, which are abstracts broken into individual subsections mandated by the journal, each containing only a sentence or two. These force the authors to be more explicit in the abstract about their goals and methods, and can be useful for readers. Here is an example from BMJ:

Objectives To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.

Design Systematic review of randomised controlled trials.

Data sources Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.

Study selection Studies showing the effects of using a parachute during free fall.

Main outcome measure Death or major trauma, defined as an injury severity score > 15.

Results We were unable to identify any randomised controlled trials of parachute intervention.

Conclusions As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute. (Smith and Pell 2003)

The exact format of structured abstracts is often dictated by the journal based on the needs of their subject area. The structure forces authors to be very clear about their goals, the methods used, and specific results of interest.

The executive summary is similar to the abstract but for a different audience: an executive summary explains your conclusions for non-technical readers, rather than for other technical experts. If you are writing a consulting report or a proposal for consideration by your bosses or management, you may not need a formal abstract—but you do need to summarize your results so that non-statisticians can understand them. Executive summaries are often seen in reports in industry and government that may be read by executives, politicians, managers, and other non-technical decision-makers.

Because executive summaries are explicitly for people who want to know your conclusions, not your methods and the technical details, they should be written clearly and should focus on the problem to be solved, the conclusions reached, and any limitations in those conclusions, without resorting to technical terms. Readers with sufficient technical knowledge can read the rest of your report for those details.

Executive summaries vary in length. Long government reports might be a hundred pages long and have a five-page executive summary that is full of bullet points and perhaps a graph of a key result. A shorter report might have a page-long executive summary.

If it helps, think of abstracts and executive summaries as the “tl;dr”1 section of your report. Abstracts are for fellow workers in your field, executive summaries are for your boss who doesn’t know much about data science. And just like a tl;dr, don’t be surprised if people don’t read past the abstract or executive summary, so make it count.

24.1.2 Introduction

After the abstract, the Introduction section is the first thing readers will read. The Introduction states the problem to be solved, why it is important, and the current knowledge about that problem. The idea is to show what is known, show there is a gap that needs to be filled, and then explain how your work fills that gap.

While those sound like many of the things included in an abstract, the Introduction is not simply a restatement of the abstract. Introductions expand on the problem and current knowledge about it, often including several paragraphs (or even several pages) of background material summarizing past work, including references and detailed discussion. This background usually leads to showing a gap in the past work—a question that remains to be answered or a problem that hasn’t been solved.

The Introduction will often preview the methods you used and the results you obtained, but not in depth. When writing for a non-technical audience, technical details should be kept out of the introduction as much as possible; it’s only when you’re writing for a technically sophisticated audience that will understand your methods (and will want to know about them in detail) that the Introduction should spend more than a few sentences on the methods.

24.1.3 Methods

The Methods section is the meat of your statistical writing. It describes the data you used—often including graphs or summary tables to describe the amount of data and its basic features—along with the methods you used to answer the research questions. This includes any diagnostics necessary to justify your choice of methods, but it does not include the results of those analyses, which come in the next section.

Discussing methods without showing their results can feel odd, and it may feel artificial to discuss how you chose your approach without showing any results that justify it. But when writing for a non-technical audience, you can expect your readers to skip through most of the Methods section, perhaps only reading the captions of any interesting-looking tables and plots. In academic journals for general audiences, the Methods section is often printed in smaller font or at the end of the article, because most readers are assumed not to care! Separating Methods and Results hence allows technical readers to get the detail they want, while other readers skip to the Results to find out what you learned.

24.1.4 Results

The Results section contains your main findings and results. First you will show the results of your analysis, giving any necessary tables, figures, and numerical results. Then your text will describe these results and comment on what they mean.

24.1.5 Discussion

The Discussion summarizes your results and then puts them in context. Were there any limitations to the analysis that mean the results cannot perfectly answer the desired questions? Are there additional questions that still need to be answered? Based on these results, what conclusions can be drawn and what recommendations can you make?

You should expect that hurried readers will skim your abstract, look at a few plots or tables that look interesting, and then jump directly to the Discussion to know what they should take from your work. (Yes, they will completely skip your Introduction, Methods, and Results except for the cooler-looking plots.) Your Discussion should hence be readable on its own.

24.2 Writing style

24.2.1 First-person active vs. third-person passive

It is common for authors to write reports in the third person, using the passive voice to describe their own actions. For example, here’s a mock paragraph I constructed based on the study by Rosenbusch, Evans, and Zeelenberg (2022):

Throughout all studies, it was predicted that rater characteristics would be more predictive of amusement than joke characteristics given the low interrater reliabilities (i.e., high interrater variance in humor ratings) observed in past research. […] Four published datasets that employed crossed (Rater × Stimuli) designs were used, to isolate variance in amusement due to rater characteristics from variance due to material characteristics.

Compare that to how the authors actually described their study, which is much more direct:

Throughout all studies, we predicted that rater characteristics would be more predictive of amusement than joke characteristics given the low interrater reliabilities (i.e., high interrater variance in humor ratings) observed in past research. […] We used four published data sets that employed crossed (Rater × Stimuli) designs to isolate variance in amusement due to rater characteristics from variance due to material characteristics.

You will often see advice to avoid the first person “I” or “we” in academic writing, but that advice is wrong. As Rodell (1936) wrote about writing by legal scholars,

One of the style quirks that inevitably detracts from the forcefulness and clarity of law review writing is the taboo on pronouns of the first person. An “I” or “me” is regarded as a rather shocking form of disrobing in print. To avoid nudity, the back-handed passive is almost obligatory:—“It is suggested—,” “It is proposed—,” “It would seem—.” Whether the writers really suppose that such constructions clothe them in anonymity so that people can not guess who is suggesting and who is proposing, I do not know. I do know that such forms frequently lead to the kind of sentence that looks as though it had been translated from the German by someone with a rather meager knowledge of English.

The passive voice can be appropriate if, for some reason, it is necessary to emphasize the actions that were taken and not who took those actions;2 but there is no general rule against using the active voice in academic writing, and you should instead choose whichever is clearest in a particular situation.

24.2.2 Complexity of writing

You do not need to write complicated sentences just because smart people write long sentences and, ergo, to look smart your sentences should be long. The goal is to arrange sentences so the reader can understand them easily. Sometimes that involves long sentences, sometimes short ones; but it always involves thinking about what the reader is looking for. See Gopen and Swan (1990) for examples of how to take reader needs into account while structuring your sentences.

24.3 Figures

As I have suggested above, you can expect your readers to read the abstract, skim the introduction, look at your figures to see if they’re interesting, and then jump directly to the Discussion section. Your figures should hence be well-chosen to illustrate important points. In fact, if substantive research questions can be answered with a plot—even if that plot has to be followed up with a model or test to formalize the results—then including that plot is extremely useful for the reader.

All figures should be numbered. Remember that depending on formatting and space, the figure may end up on a different page from the text that refers to it. The text should hence refer to the figure by number, rather than referring to the “plot below” or the “figure above”. You can see many examples of this throughout this book.

Take care to ensure figures are simple, easy to read, and labeled. All axes should have meaningful labels with units, whenever possible; shorthand variable names should be replaced with meaningful names (like “Median SAT math scores” rather than “SATMTMID”); legends should be labeled; text should be a reasonable size and not ludicrously small or huge.

Most importantly, take care to ensure your figures have a point. Each figure should contribute something: it should help the reader understand the kind of data you’re using, show why you picked a certain model, illustrate a problem you’re trying to solve, suggest the answer to a research question, or demonstrate a problem with a particular model. You do not need to include every plot of every variable. If captured in a dark alley by the Data Visualization Police, you should be able to justify why you used every plot in your paper.

If your figures have a point, you should be able to explain that point in a caption. All figures should have captions underneath, and the captions should say what is in the figure and what important things the reader should notice. Remember, readers are ignoring your main text entirely and just looking at the plots, so the captions should explain the figure well.

TODO Get an example caption and figure from a CC-BY paper

24.4 Describing models

Part of your report will explain the model you used, the covariates you chose, and so on. How you do this depends on the model and the audience.

If you’re using a model type that is widely known to your audience—such as linear regression, for readers in almost any field of applied science—you do not need to write out a model formula. People know what linear regression is, so you do not need to write that your model is \[ \text{bill length} = \beta_0 + \beta_1 \cdot \text{(flipper length)} + \dots + e. \] Instead, you can explain that you used linear regression, then present a table of predictors and coefficients, like Table 7.1. The table clearly indicates the predictors you used, any interactions or transformations, and so on. A knowledgeable reader will understand this. Such a table can be produced in R with the modelsummary package’s modelsummary() function, which can be customized using all the fancy table features supported by gt. Other packages for similar goals include texreg and stargazer.

If you use multiple models for the same data, perhaps because your research question can be answered by comparing models with different terms or interactions, you can present them in the same table. For example, if in Example 7.2 we were unsure whether interaction terms were necessary, we could present models with and without interactions. This code uses modelsummary() to produce Table 24.1.


model1 <- lm(
  bill_length_mm ~ flipper_length_mm + species,
  data = penguins

model2 <- lm(
  bill_length_mm ~ flipper_length_mm + species +
  data = penguins

modelsummary(list("Model 1" = model1, "Model 2" = model2),
             gof_map = c("r.squared", "nobs"))
Table 24.1: Predicting bill length for penguins, with and without an interaction for species. Values in parentheses are standard errors.
Model 1  Model 2
(Intercept) −2.059 13.587
(4.039) (6.051)
flipper_length_mm 0.215 0.133
(0.021) (0.032)
speciesChinstrap 8.780 −7.994
(0.399) (10.481)
speciesGentoo 2.857 −34.323
(0.659) (9.820)
flipper_length_mm × speciesChinstrap 0.088
flipper_length_mm × speciesGentoo 0.182
R2 0.776 0.785
Num.Obs. 342 342

However, if you’re using a model that’s unusual in your field, it may be wise to write out the math. This is particularly common when developing statistical methodology, where you’re using a model you developed yourself.

24.5 Tables

Do not underestimate the value of a good table.

People love making complicated and colorful plots that attract the reader’s attention. But a well-structured table can capture the reader’s interest, encouraging them to spend time exploring numbers in the table: why is this one higher? How does this group compare to that one? And because tables give numbers directly, it can be easier to make specific comparisons. It’s also easier to present confidence intervals and uncertainty than it is in some kinds of plots.


24.6 Citation and credit


24.7 Writing resources

For general questions of style and grammar, including examples of how specific words should be used, what punctuation is appropriate when, and discussion of thousands of specific terms and expressions, I can strongly recommend Garner’s Modern English Usage (Garner 2022). I keep a copy by my desk for reference. Think of it as a dictionary for English style and idiom.

For typography—meaning the use of page layout, punctuation, formatting, and whitespace to arrange your text—the standard reference is Bringhurst (2012). His book is far more detailed than you will likely ever need, but if you ever start thinking of making your own templates for reports (or your thesis), it’s an excellent reference.

  1. Too long; didn’t read.↩︎

  2. For example, when admitting that your company has made a terrible mistake.↩︎