5 Common Statistical Mistakes in Medical Dissertations

Statistical analysis is a key part of any medical dissertation. Unfortunately, many PhD candidates make mistakes that can compromise the entire study. Here are the five most common problems and how to avoid them.

1. Incorrect Choice of Statistical Test

One of the most common mistakes is using parametric tests (like t-test) on data that is not normally distributed. Always check the distribution of your data before choosing a method.

**Solution:** Use normality tests (Shapiro-Wilk, Kolmogorov-Smirnov) and switch to non-parametric alternatives when needed.

2. Insufficient Sample Size

A small sample leads to low statistical power and inability to detect real effects. Many studies are "underpowered".

**Solution:** Conduct power analysis before starting your study to determine the required number of participants.

3. Multiple Comparisons Without Correction

When performing many statistical tests simultaneously, the probability of false positive results increases dramatically.

**Solution:** Use corrections such as Bonferroni, Holm, or False Discovery Rate (FDR) for multiple comparisons.

4. Confusing Correlation with Causation

A statistical relationship between two variables does not mean one causes the other. This is a fundamental interpretation error.

**Solution:** Be careful with wording. Use "association" instead of "causes" in observational studies.

5. Ignoring Missing Data

Simply excluding participants with missing data can lead to biased results and loss of statistical power.

**Solution:** Analyze the pattern of missing data and consider methods like multiple imputation.

Conclusion

Avoiding these mistakes will improve the quality of your dissertation and increase chances of successful defense and publication. If you have doubts about statistical analysis, seek professional consultation.

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