Presenting Statistical Results in Scientific Papers

When presenting statistical results in a scientific paper, it's crucial to follow best practices to ensure clarity and precision. This guide will provide you with a structured approach to presenting summary statistics, test statistics, p-values, and other relevant information. Here’s how you can effectively communicate your statistical findings in any scientific paper. This blog will be beneficial for those students also who want to show statistical results in a better manner while solving their statistics homework.

1. Choosing the Right Numbers to Present

Key Statistics to Include:

• Test Statistic: Depending on the test, include the relevant statistic (e.g., t for t-tests, F for ANOVAs).
• Degrees of Freedom: This contextualizes the test statistic (e.g., t(9)).
• P Value: Indicates the significance of your results (e.g., P = 0.022).
• Effect Size: Provides context to the practical significance of your findings (e.g., mean difference or percentage change).

Example:

If you conducted a Welch’s t-test, you might present the results as: “There were 23% fewer loopers on sprayed kale (t(9) = 2.77, P = 0.022).”

2. Deciding Where to Present Your Numbers

In-Text:

• Use for simple statistics.
• Keep sentences concise to maintain readability.

Figures:

• Ideal for moderately complex statistics.
• Avoid cluttering the figure with too many numbers.

Tables:

• Best for complex statistics that involve multiple comparisons or variables.
• Design tables carefully to enhance readability.

Online Supplement:

• Use for secondary statistics, detailed assumptions tests, or alternative analyses.
• Remember that these are rarely read, so include only what’s necessary.

3. Balancing Statistics and Narrative

Pattern First, Statistics Second:

• Lead with the biological or substantive pattern.
• Follow with the statistical evidence.
• Avoid sentences that focus solely on the statistics.

Example:

“Sprayed kale had significantly fewer loopers than unsprayed kale, with a 23% reduction in looper density (t(9) = 2.77, P = 0.022).”

4. Reporting P Values

Exact P Values:

• Report the exact value (e.g., P = 0.022) rather than a threshold (e.g., P < 0.05).
• This allows for greater transparency and utility in meta-analyses.

Significant Digits:

• Use 2-3 significant digits for P values and test statistics.
• Avoid unnecessary precision (e.g., P = 0.022823511).

5. Handling Marginally Significant Results

P = 0.051:

• Understand that the distinction between P = 0.049 and P = 0.051 is minimal.
• It’s acceptable to describe P = 0.051 as “marginally significant” based on the context and existing guidelines.

6. Writing About Statistical Results

Clear and Concise Reporting:

• Provide a clear narrative that guides the reader through your findings.
• Emphasize the biological or substantive implications first, followed by the statistical support.

Example:

“In our study, the new insecticide reduced the average density of cabbage loopers on kale by 23%. This reduction was statistically significant (t(9) = 2.77, P = 0.022), indicating the effectiveness of the treatment.”

Conclusion

While statistical analysis is critical, clear communication of your findings is equally important. By following these guidelines, you can ensure that your statistical results are presented in a way that is both accurate and accessible to your readers. Always remember to respect your audience by leading them through the story your data is telling, making your scientific contributions as impactful as possible.