Which Statistical Test Should You Use in Your Thesis? Complete Decision Guide
A practical decision-making guide to choosing the correct statistical test for your research based on data types, assumptions, and study design.
One of the most challenging steps in academic thesis writing is selecting the correct statistical test for data analysis. An incorrect choice of test can compromise the validity of the entire research and lead to misleading conclusions.
This guide provides a systematic decision framework to help you choose the most appropriate statistical test for your thesis.
Step 1: Identify the Type of Variables
Statistical test selection primarily depends on the type of variables:
1. Numerical (Continuous / Metric) Variables
These include measurable data such as height, weight, age, blood pressure, and laboratory values.
2. Categorical (Qualitative) Variables
These include grouped data such as gender, disease status (yes/no), or education level.
Step 2: Define Dependent and Independent Variables
Dependent variable (Outcome): The main variable being measured in the study
Independent variable (Predictor): The variable(s) assumed to influence the outcome
This distinction directly determines the appropriate statistical test.
Step 3: Check the Normality Assumption
For numerical data, checking distribution is a critical step:
If normally distributed: Parametric tests are used
If not normally distributed or ordinal data: Non-parametric tests are preferred
Normality can be assessed using Shapiro-Wilk test, histograms, and Q-Q plots.
Statistical Test Selection Guide
1. Comparing Two Independent Groups (e.g., male vs female)
Parametric (normal distribution): Independent Samples t-test
Non-parametric: Mann-Whitney U Test
Categorical data: Chi-Square Test or Fisher’s Exact Test
2. Comparing Two Dependent (Paired) Groups (e.g., before vs after treatment)
Parametric: Paired Samples t-test
Non-parametric: Wilcoxon Signed-Rank Test
Categorical data: McNemar Test
3. Comparing Three or More Independent Groups
Parametric: One-Way ANOVA
Non-parametric: Kruskal-Wallis H Test
Categorical data: Chi-Square Test (r × c tables)
4. Repeated Measures (Within-Subject Comparisons)
Parametric: Repeated Measures ANOVA
Non-parametric: Friedman Test
Categorical data: Cochran’s Q Test
Correlation and Regression Analysis
To examine relationships or predictive models between variables:
Pearson correlation: When data are normally distributed
Spearman correlation: When data are not normally distributed
Linear regression: For continuous outcome variables
Logistic regression: For binary categorical outcomes
Important Note (Jury Perspective)
In thesis defenses, it is not enough to say “I used this test.” Examiners typically evaluate:
Whether variables are correctly defined
Whether normality assumptions were checked
Whether parametric or non-parametric tests were justified
Why alternative tests were not chosen
Conclusion
Correct statistical test selection is one of the most critical steps determining the scientific validity of a thesis. Success in statistical analysis requires not only reporting results but also clearly understanding why a specific test was chosen.