Master hypothesis testing from fundamentals to advanced applications. Each module builds on the previous one.
Apply your knowledge with real-world scenarios and challenges
Choose a test type and work through realistic problems step-by-step.
Comprehensive comparison of all hypothesis tests
| Test Name | Purpose | Data Type | Assumptions | Python Function |
|---|---|---|---|---|
| One-Sample t-Test | Compare sample mean to known value | Continuous | Normal distribution | stats.ttest_1samp() |
| Two-Sample t-Test | Compare two independent groups | Continuous | Normal, equal variance | stats.ttest_ind() |
| Paired t-Test | Compare paired measurements | Continuous | Normal differences | stats.ttest_rel() |
| One-Way ANOVA | Compare 3+ independent groups | Continuous | Normal, homogeneity | stats.f_oneway() |
| Two-Way ANOVA | Two factors, interaction effects | Continuous | Normal, homogeneity | statsmodels.anova_lm() |
| Chi-Square Test | Test independence in categories | Categorical | Expected freq > 5 | stats.chi2_contingency() |
| Mann-Whitney U | Non-parametric two groups | Ordinal/Non-normal | None | stats.mannwhitneyu() |
| Wilcoxon Signed-Rank | Non-parametric paired test | Ordinal/Non-normal | None | stats.wilcoxon() |
| Kruskal-Wallis | Non-parametric 3+ groups | Ordinal/Non-normal | None | stats.kruskal() |
| Pearson Correlation | Linear relationship strength | Continuous | Linear, normal | stats.pearsonr() |
| Spearman Correlation | Monotonic relationship | Ordinal | None | stats.spearmanr() |
| Linear Regression | Predict continuous outcome | Continuous | Linearity, normality | stats.linregress() |
| Logistic Regression | Predict binary outcome | Mixed | Independence | sklearn.LogisticRegression() |
| Fisher's Exact Test | 2×2 contingency, small n | Categorical | None | stats.fisher_exact() |
| Z-Test for Proportions | Compare proportions | Binary | Large sample | proportions_ztest() |
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