Frequent Mistakes in the Results Section and How to Steer Clear

The path to completing your dissertation’s results chapter is paved with potential pitfalls that can compromise months of careful data collection. Even with the best intentions, it is surprisingly easy to fall into habits that diminish the credibility of your findings or, worse, render them invalid. Many of these errors are not statistical but structural in nature, stemming from a unclear grasp of the chapter’s core purpose. This guide highlights the most common blunders students encounter and provides a practical roadmap for addressing them effectively to ensure your analysis is persuasive and academically sound.

1. The Cardinal Sin: Mixing Results with Discussion

This is, without a doubt, the most frequent mistake made in dissertation writing. The Results chapter and the Discussion chapter have distinctly different purposes.

• The Pitfall: Speculating on the meaning in the Results chapter. Using language like “This suggests that…” or “This surprising finding is probably because…”
• Why It’s a Problem: It confuses the reader and undermines your credibility by failing to maintain a clear narrative between objective data and subjective interpretation.
• The Prevention Strategy: Adopt a “reporting only” mentality. Your Results chapter should only answer “What did I find?” Use neutral reporting verbs like “the results indicated,” “the data showed,” or “a significant difference was observed.” Save the “What does this mean?” for the Discussion chapter.

2. Data Dumping: Including Everything

Another common error is to include every statistical test you generated, regardless of its relevance.

• The Pitfall: Dumping pages of exploratory analyses that do not directly address your stated objectives.
• Why It’s a Problem: It loses and confuses the reader, obscuring the truly important findings. It lacks narrative focus and can make it seem like you are searching for a story rather than answering a pre-defined question.
• The Prevention Strategy: Let your hypotheses be your guide. Before including any result, ask: “Does this directly help answer one of my research questions?” If the answer is no, exclude it.

3. The File Drawer Problem: Hiding Non-Significant Results

The pressure to find positive results is immense, but rigorous research requires full transparency.

• The Pitfall: Omitting tests that yielded non-significant results. This is known as “publication bias,” where only studies with positive results are published, skewing the scientific record.
• Why It’s a Problem: It is a form of bias and presents an inaccurate picture of your research process. A non-significant result is still a valuable finding that tells you something important (e.g., “there is no evidence of a relationship between X and Y”).
• The Prevention Strategy: Report all tests related to your hypotheses. State non-significant results in the same neutral tone as significant ones. Example: “The independent-samples t-test revealed no statistically significant difference in scores between the control and experimental groups (t(58) = 1.45, p = .154).”

4. The Classic Confusion

This is a fundamental error of data interpretation that can completely invalidate your conclusions.

• The Pitfall: Assuming that because two variables are correlated, one causes the other. For example, “The study found that ice cream sales cause drownings” (when in reality, both are caused by a third variable: hot weather).
• Why It’s a Problem: It demonstrates a basic flaw in scientific reasoning. Causation can only be strongly implied through controlled experimental designs.
• The Prevention Strategy: Always use cautious language. Use phrases like “associated with,” “linked to,” “correlated with,” or “predicted.” Only use “cause” or “effect” if your study design was a randomized controlled trial (RCT).

5. The Island Chapter: Isolating Your Findings

Your dissertation is a unified narrative, not a series of disconnected chapters.

• The Pitfall: Presenting your results as a standalone list of findings without any reference to the concepts you outlined in your literature review.
• Why It’s a Problem: It fails to establish context to start building your argument of what you found. The reader is left wondering how your results extend the existing body of knowledge.
• The Prevention Strategy: While full interpretation is for the Discussion chapter, you can still create a bridge in the Results. Use framing language like:
  • “Consistent with the work of Smith (2020), the results showed…”
  • “Contrary to the hypothesis derived from Theory X, the analysis revealed…”
  • “This finding aligns with the proposed model…”

  This sets the stage for the deeper discussion to come.

6. Poor Visual Communication

Unclear tables and figures can make even the clearest results incomprehensible.

• The Pitfall: 3D pie charts that distort the data.
• Why It’s a Problem: Visuals should enhance clarity, not hinder it. Poor visuals frustrate the reader and can lead to misinterpretation.
• The Prevention Strategy:
  • Ensure every visual is numbered and has a concise caption.
  • Keep tables and graphs minimalist. Avoid unnecessary gridlines.
  • Choose the right chart for the data (e.g., bar charts for comparisons, line graphs for trends over time).
  • Always refer to the visual in the text before it appears.

7. Violating Test Assumptions

Every analytical procedure comes with a set of conditions that must be met to be used validly.

• The Pitfall: Running a ANOVA without first testing that your data meets the necessary assumptions (e.g., independence).

• Why It’s a Problem: If the assumptions are violated, IGNOU project format – just click the next web site – the results of the test are unreliable. Your p-values and confidence intervals cannot be trusted.
• The Prevention Strategy: Before running any primary test, run diagnostic tests. This is a critical part of your analysis. If assumptions are violated, employ a non-parametric equivalent (e.g., Mann-Whitney U test instead of an independent-samples t-test) or transform your data.

Conclusion

Avoiding these common pitfalls is not about memorizing rules but about adopting a mindset of precision, neutrality, and intellectual honesty. Your data analysis chapter is the empirical heart of your dissertation; its credibility is essential. By strictly separating results from discussion, respecting the limits of correlation, creating clear visuals, and upholding methodological standards, you transform your chapter from a potential minefield of errors into a powerful, persuasive, and academically robust presentation of your research. This meticulous approach pays immense dividends in the overall impact of your work.