Common Hypothesis Testing Methods Used in Academic Research

Academic research employs a defined toolkit of hypothesis testing methods, each designed for specific data structures and research questions. Choosing the wrong test invalidates conclusions even when calculations are mathematically correct. This guide catalogues the most common hypothesis testing methods used in dissertations, theses, and journal papers—when to use each, what assumptions apply, and how results are typically reported.

T-tests: comparing means

• One-sample t-test: one group mean vs population benchmark.
• Independent samples t-test: two unrelated groups.
• Paired samples t-test: same subjects measured twice.
• Assumptions: continuous DV, approximate normality, independent observations.
• Report: t(df), p, Cohen's d.

ANOVA: comparing three or more means

One-way ANOVA tests whether three or more group means differ significantly. Significant F-test followed by post-hoc tests (Tukey, Bonferroni) identifies which pairs differ. Two-way ANOVA examines two factors and interaction effects. Report F(df_between, df_within), p, η² or partial η².

Chi-square tests: categorical data

• Chi-square goodness of fit: observed vs expected frequencies.
• Chi-square test of independence: association between two categorical variables.
• Assumption: expected cell count ≥ 5 in most cells.
• Report: χ²(df, N), p, Cramér's V or phi.

Correlation: measuring association

Pearson r for two continuous variables with linear relationship and approximate normality. Spearman ρ for ordinal data or non-linear monotonic relationships. Point-biserial for one continuous and one dichotomous variable. Report r(df), p, and r² for variance explained.

Linear regression: prediction and explanation

Simple regression: one predictor. Multiple regression: several predictors. Logistic regression: binary outcome. Report β coefficients, t or Wald statistics, p-values, R², adjusted R², and check multicollinearity (VIF). Regression tests whether predictors significantly explain outcome variance.

Non-parametric alternatives

• Mann-Whitney U: non-parametric two-group comparison.
• Wilcoxon signed-rank: non-parametric paired comparison.
• Kruskal-Wallis: non-parametric ANOVA alternative.
• Friedman test: non-parametric repeated measures.
• Use when normality or homogeneity assumptions are severely violated.

Advanced methods in postgraduate research

MANOVA for multiple DVs simultaneously. ANCOVA controlling for covariates. Factor analysis and SEM for latent constructs. Mixed ANOVA for within-between designs. Survival analysis for time-to-event data. Match method complexity to research question—simpler valid tests beat inappropriate advanced methods.

Decision guide: which test when?

1. 1One continuous DV, two groups → independent t-test.
2. 2One continuous DV, 3+ groups → one-way ANOVA.
3. 3Two continuous variables → Pearson or Spearman correlation.
4. 4Predict continuous outcome from predictors → regression.
5. 5Two categorical variables → chi-square.
6. 6Same subjects, two time points → paired t-test or repeated measures ANOVA.

Assumption checking summary

Every parametric test has assumptions. Document how you checked each and what you did if violated. Examiners penalise blind application of tests without assumption discussion. SPSS provides Shapiro-Wilk, Levene's, and collinearity diagnostics in standard output.

Statistical analysis support for researchers

Unsure which test fits your design? Our data analysis service matches your research questions to valid statistical methods and produces examiner-ready SPSS output and APA results write-ups.