Which Statistical Test Should I Use? t-Test, ANOVA, Correlation and More

Statistics is where many students struggle most. Yet choosing the right analysis comes down to a few questions: What are you asking, what type are your variables and how many groups do you have?

1. Identify variable types

• Continuous (numeric): scale scores, age, grade point average
• Categorical: gender, department, year of study

2. Choose the analysis by question type

Looking for differences between groups?

• Two independent groups (e.g. women and men): independent-samples t-test
• Two measurements of the same people (e.g. pre-test and post-test): paired-samples t-test
• Three or more groups (e.g. years 1–4): one-way ANOVA; if significant, post hoc tests show which groups differ

Looking for relationships?

• Two continuous variables: Pearson correlation
• Ordinal or non-normal data: Spearman rank correlation
• Two categorical variables: chi-square test of independence

Predicting an outcome?

• One or more variables explaining a continuous variable: linear (multiple) regression

Scale reliability

• Internal consistency of items: Cronbach’s alpha

3. Check assumptions

Parametric tests (t-test, ANOVA, Pearson) expect approximately normal data.

• Skewness and kurtosis are examined; some sources use ±1.5 and others ±2 as thresholds.
• With small samples, normality tests and plots are also checked.
• If assumptions are not met, use non-parametric alternatives:
• Mann-Whitney U instead of the independent t-test
• Kruskal-Wallis H instead of ANOVA

4. Report results correctly

A p value alone is not enough. Report the test statistic, degrees of freedom, p value and effect size together. A significant result does not necessarily mean a large or important difference; the effect size shows that.

Common mistakes

• Running repeated t-tests for three groups (use ANOVA)
• Interpreting correlation as cause and effect
• Computing scale scores without reverse-coding items
• Never checking normality

Statistics with Academiana

In Academiana’s statistics tool you upload your data and choose an analysis: descriptives and normality, t-tests, ANOVA, Mann-Whitney, Kruskal-Wallis, correlation, regression, chi-square and Cronbach’s alpha. Calculations run on the server using established formulas and results come back as APA 7 tables with an interpretation sentence. If you are unsure which test to use, you can also reach expert statistics consulting.