Choosing the Right Statistical Test: A Researcher's Decision Matrix

In medical research, selecting the wrong statistical test is a fatal flaw that often leads to immediate desk rejection. The choice is governed by two primary factors: the type of data you have and the question you are trying to answer.

1. Identify Your Data Type

Are your variables categorical (e.g., gender, blood type) or continuous (e.g., blood pressure, weight)? Categorical data often requires Chi-square tests, while continuous data requires parametric or non-parametric comparisons.

2. Normal Distribution Check

Parametric tests (like T-tests and ANOVA) assume your data follows a normal distribution. If your data is skewed, you must opt for non-parametric alternatives such as the Mann-Whitney U test or the Kruskal-Wallis test to maintain statistical validity.

3. Number of Groups

Comparing two groups? Use a T-test. Comparing three or more? ANOVA is your starting point. Remember to use post-hoc tests only if the initial ANOVA yield a significant result.