How to Write a Good Results Section

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 {EC₅₀: 3.78 nM [95% confidence interval (CI): 1.82 to 6.78]}, fosphenytoin [an antiepileptic drug, EC₅₀: 77.01 nM (95% CI: 52.63 to 115.10)], and dexamethasone phosphate [steroid-derived phosphate, EC₅₀: 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 EC₅₀ 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.

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