📊 How to Choose the Right Statistical Test for Your Research Project

🔹 Introduction

Selecting the right statistical test is like choosing the right tool for the job. The wrong test can mislead your results, while the right one strengthens your research findings.

1️⃣ T-Test: Comparing Two Groups

When you want to see if there’s a difference between two means (e.g., treatment vs. control), the t-test is your go-to. It’s simple yet powerful.

2️⃣ ANOVA: Comparing More Than Two Groups

Need to compare three or more group means? ANOVA (Analysis of Variance) helps identify whether at least one group differs significantly from the others.

3️⃣ Chi-Square Test: Categorical Data

When your data is in categories (e.g., gender, smoking status), the Chi-Square test checks whether distributions differ from what’s expected or if two variables are related.

4️⃣ Correlation & Regression: Relationships Between Variables

If your question is about relationships, use correlation to measure strength and regression to predict outcomes based on independent variables.

5️⃣ Non-Parametric Tests: When Assumptions Fail

If your data doesn’t follow normal distribution, tests like the Mann–Whitney U or Kruskal–Wallis serve as non-parametric alternatives to t-tests and ANOVA.

🧾 Examples in Action

• T-Test: Comparing exam scores of two teaching methods.

• ANOVA: Comparing average blood pressure across three diets.

• Chi-Square: Testing the link between smoking status and lung cancer.

• Regression: Predicting weight based on height and age.