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Results and Discussion: Facts and Interpretation

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science

Almost every empirical research paper contains two essential sections: Results and Discussion. The former presents the facts collected through the research method, while the latter interprets them to answer the research questions. When interpreting the data, you must address the most obvious concerns that readers may have. For example, in the Results section, you might state: “85% of respondents refused to participate in our survey” (this is a fact). Then, in the Discussion section, you might say: “We believe that programmers are innately lazy and irresponsible” (this is an interpretation). You might also add, “Perhaps not all of them were lazy, but just busy.” While the Results section leaves no room for doubt, summarizing findings “as is,” the Discussion section engages in an open debate with an imaginative reader.

Mulholland Drive (2001) by David Lynch
Mulholland Drive (2001) by David Lynch

In the Method section, you’ve already explained how you collected, processed, and analyzed the data. Now, in the Results section, you present the actual data collected and generated. The simpler the method of data representation, the better. Thus, in order of preference (with the last being your last choice):

  • Plain text
  • Lists (\begin{itemize})
  • Table (\begin{tabular})
  • Graphs and diagrams (\begin{figure})

If the data is too extensive to show in the paper, you can store it in a GitHub repository and mention its address in the Results section. For example:

\section{Results}

We contacted 135 programmers from three 
software companies: ACME Inc, Google, and
Amazon. We asked them kindly to answer
a short questionnaire of just 128 questions.
115 people refused, which is 85%.
The full list of those who refused, along with 
their names and home addresses, 
is published in GitHub repository\footnote{
  \url{https://github.com/...}}.

In the Method section, you posed several Research Questions. Now, in the Discussion section, you answer them using the data you’ve just presented in the Results. This is the time for an opinionated interpretation of the data: be brave and direct, yet careful.

When you’ve answered the Research Questions, you initiate a debate with your readers, imagining them asking difficult and important questions. The answers you provide are your speculation, imagination, improvisation, etc. Also, through the Q&A format, you acknowledge the limitations of your research and suggest potential future research topics.

Consider these questions (re-phrase them for your own context):

  • Why are these results important?
  • Why has no one discovered this before?
  • Is it possible that we made a mistake?
  • How else could the data be interpreted?
  • What’s next?

I suggest dedicating exactly one paragraph per question, starting with a bold-faced formulation of it, followed by your answer to your imagined opponent. Here’s an example:

\section{Discussion}

\textbf{RQ1: How many programmers are lazy?}
Since 85% of our respondents refused to complete
our short questionnaire, we strongly believe 
that most programmers are lazy.

\textbf{RQ2: Why are programmers lazy?}
Since the majority of programmers refused to complete
the 128-question questionnaire, we believe
they become lazy when confronted with a number
that is a power of two.

\textbf{Is it possible that programmers are 
just busy?} Yes, it's possible, but highly
unlikely, as \citet{x2019} previously found 
that programmers spend 90% of their office time 
reading jokes on the internet.

The more you overlook in the Discussion section, the greater the chance of your paper being rejected. Reviewers are often knowledgeable individuals with many years of experience in the field; they will certainly have concerns about your Method, Results, and answers to the Research Questions. If you don’t address these concerns explicitly in the Discussion section, they may think you are either concealing the research’s weaknesses or are not astute enough to recognize them. In either case, it could lead to a rejection of your paper.


You may find inspiration in these papers (use Google Scholar to download their PDFs):

  • Zhaowei Zhang et al., Diet Code Is Healthy: Simplifying Programs for Pre-trained Models of Code, ESEC/FSE 2022
  • Norman Peitek et al., Correlates of Programmer Efficacy and Their Link to Experience: A Combined EEG and Eye-Tracking Study, ESEC/FSE 2022

These opinions might also be helpful:

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