How to Choose the Right Statistical Test for Your Research

Choosing the right statistical test is one of the most common challenges in research. Use the wrong test and your results are invalid. Use the right one and your analysis becomes straightforward. This guide walks you through the decision process based on your research question, variable types, and data characteristics.

The Three Questions to Ask

Before selecting a test, answer these three questions:

1. What is your research question? Are you comparing groups, measuring a relationship, or predicting an outcome?
2. What types of variables do you have? Categorical (nominal/ordinal) or continuous (interval/ratio)?
3. How many groups or variables are involved?

Decision Guide by Research Goal

Goal 1: Compare Two Groups

Situation	Parametric Test	Non-Parametric Alternative
Two independent groups, continuous outcome	Independent samples t-test	Mann-Whitney U test
Same group measured twice, continuous outcome	Paired samples t-test	Wilcoxon signed-rank test
Two groups, categorical outcome	Chi-square test	Fisher's exact test

When to use non-parametric: When your data is not normally distributed, has significant outliers, or uses ordinal scales.

Goal 2: Compare Three or More Groups

Situation	Parametric Test	Non-Parametric Alternative
Three+ independent groups, continuous outcome	One-way ANOVA	Kruskal-Wallis H test
Three+ groups with a covariate	ANCOVA	—
Same group measured three+ times	Repeated measures ANOVA	Friedman test
Two factors, continuous outcome	Two-way (factorial) ANOVA	—

After a significant ANOVA, run post-hoc tests (Tukey HSD, Bonferroni) to find which groups differ.

Goal 3: Measure a Relationship

Situation	Test
Two continuous variables	Pearson correlation
Two ordinal variables or non-normal data	Spearman rank correlation
One continuous + one dichotomous	Point-biserial correlation
Two categorical variables	Chi-square test of independence

Goal 4: Predict an Outcome

Situation	Test
Predict a continuous outcome from one predictor	Simple linear regression
Predict a continuous outcome from multiple predictors	Multiple regression
Predict a categorical outcome (yes/no)	Logistic regression
Test if a variable explains the relationship (mechanism)	Mediation analysis (PROCESS)
Test if a variable changes the strength of a relationship	Moderation analysis (PROCESS)

Variable Type Quick Reference

Understanding your variable types is critical:

• Nominal: Categories with no order (gender, ethnicity, department)
• Ordinal: Categories with order (Likert scales, education levels, rankings)
• Interval: Numeric with equal intervals, no true zero (temperature in Celsius, IQ scores)
• Ratio: Numeric with equal intervals and a true zero (age, income, weight)

Rule of thumb: Parametric tests (t-test, ANOVA, regression) require continuous dependent variables. If your DV is categorical, use chi-square or logistic regression.

Checking Assumptions

Most parametric tests share common assumptions:

Normality

• How to check: Shapiro-Wilk test (p > .05 = normal), histograms, Q-Q plots
• If violated: Use the non-parametric equivalent or transform your data

Homogeneity of Variances

• How to check: Levene's test (p > .05 = equal variances)
• If violated: Use Welch's t-test or Welch's ANOVA instead of the standard versions

Independence

• How to ensure: Each observation should come from a different participant
• If violated: Use a paired/repeated measures test

Linearity (for regression)

• How to check: Scatter plot of residuals vs. predicted values
• If violated: Consider polynomial regression or data transformation

Common Mistakes

1. Using a t-test for three or more groups — This inflates Type I error. Use ANOVA instead
2. Treating Likert scales as continuous — A single Likert item (1-5) is ordinal. A composite scale (sum of multiple items) is typically treated as continuous
3. Ignoring assumption violations — Always check normality and homogeneity. Violations can invalidate results
4. Running too many tests without correction — Multiple comparisons increase false positive risk. Apply Bonferroni correction when running many tests
5. Using correlation to imply causation — Correlation measures association, not cause-and-effect

Quick Decision Flowchart

1. Is your outcome variable categorical or continuous?

   • Categorical → Chi-square, Fisher's exact, or logistic regression
   • Continuous → Continue to step 2

2. Are you comparing groups or measuring relationships?

   • Comparing groups → Continue to step 3
   • Measuring relationships → Correlation or regression

3. How many groups?

   • 2 groups → t-test (independent or paired)
   • 3+ groups → ANOVA (one-way, two-way, or repeated measures)

4. Are assumptions met?

   • Yes → Use parametric test
   • No → Use non-parametric alternative

Summary Table

Research Question	Variable Types	Test
Difference between 2 independent groups	Continuous DV	Independent t-test
Difference before/after	Continuous DV	Paired t-test
Difference among 3+ groups	Continuous DV	One-way ANOVA
Association between 2 variables	Both categorical	Chi-square
Relationship strength	Both continuous	Pearson correlation
Predict continuous outcome	Mixed predictors	Multiple regression
Predict binary outcome	Mixed predictors	Logistic regression
Test a mechanism (why)	Continuous	Mediation (PROCESS)
Test a condition (when)	Continuous	Moderation (PROCESS)

Still unsure which test to use? Our statistical analysis services team can help you select the right approach and run the analysis correctly. Get a free consultation.