Independent Sample Tests
Overview
Independent sample tests compare two or more groups of data that are not related to each other (e.g., Treatment vs. Control, Male vs. Female). The goal is to determine if the groups come from populations with different parameters—typically observing differences in their means or medians.
Two-Group Comparisons
When comparing exactly two groups:
- TTEST_IND: Independent samples t-test. Use when data is normally distributed. It comes in two flavors:
- Student’s t-test: Assumes groups have equal variances.
- Welch’s t-test: Does not assume equal variances (generally recommended).
- MANNWHITNEYU: Mann-Whitney U test. A non-parametric alternative to the t-test. Tests if one distribution is stochastically greater than the other (often interpreted as a difference in medians). Robust to outliers and non-normal data.
- KS_2SAMP: Kolmogorov-Smirnov test for two samples. Tests whether two samples are drawn from the same distribution (sensitive to differences in location, scale, and shape).
Multiple Group Comparisons (ANOVA)
When comparing three or more groups (e.g., Drug A vs. Drug B vs. Placebo):
- F_ONEWAY: One-way ANOVA. Tests the null hypothesis that all group means are equal. If p < 0.05, at least one group mean is different. Data must be normal with equal variances.
- KRUSKAL: Kruskal-Wallis H-test. The non-parametric equivalent of ANOVA. Tests if samples originate from the same distribution.
- ALEXANDERGOVERN: A robust ANOVA alternative that does not assume equal variances (homoscedasticity).
Homogeneity of Variance
Many parametric tests (like Student’s t-test and standard ANOVA) assume that all groups have the same variance (homoscedasticity).
- LEVENE: Levene’s test. Tests if groups have equal variances. Robust to non-normality.
- BARTLETT: Bartlett’s test. More powerful than Levene’s if data is normal, but very sensitive to departures from normality.
- FLIGNER: Fligner-Killeen test. A non-parametric test for homogeneity of variances.
Native Excel Capabilities
- T-Tests: Excel’s
T.TESTfunction handles independent samples well, offering options for equal and unequal variance (Welch’s). - ANOVA: The “ANOVA: Single Factor” tool in the Analysis ToolPak performs one-way ANOVA, but it produces a static table, not a formula result that updates with data.
- Missing Non-Parametrics: Excel has no native functions for Mann-Whitney U, Kruskal-Wallis, or Kolmogorov-Smirnov tests. Users must use complex rank formulas or VBA.
- Missing Variance Tests: No built-in test for equality of variances (Levene/Bartlett), forcing users to assume variances are equal or just default to Welch’s t-test without verification.
Tools
| Tool | Description |
|---|---|
| ALEXANDERGOVERN | Performs the Alexander-Govern test for equality of means across multiple independent samples with possible heterogeneity of variance. |
| ANDERSON_KSAMP | Performs the k-sample Anderson-Darling test to determine if samples are drawn from the same population. |
| ANSARI | Performs the Ansari-Bradley test for equal scale parameters (non-parametric) using scipy.stats.ansari. |
| BRUNNERMUNZEL | Computes the Brunner-Munzel nonparametric test for two independent samples. |
| BWS_TEST | Performs the Baumgartner-Weiss-Schindler test on two independent samples. |
| CVM_2SAMP | Performs the two-sample Cramér-von Mises test using scipy.stats.cramervonmises_2samp. |
| DUNNETT | Performs Dunnett’s test for multiple comparisons of means against a control group. |
| EPPS_SINGLE_2SAMP | Compute the Epps-Singleton test statistic and p-value for two samples. |
| F_ONEWAY | Performs a one-way ANOVA test for two or more independent samples. |
| FLIGNER | Performs the Fligner-Killeen test for equality of variances across multiple samples. |
| FRIEDMANCHISQUARE | Computes the Friedman test for repeated samples. |
| KRUSKAL | Computes the Kruskal-Wallis H-test for independent samples. |
| KS_2SAMP | Performs the two-sample Kolmogorov-Smirnov test for goodness of fit. |
| LEVENE | Performs the Levene test for equality of variances across multiple samples. |
| MANNWHITNEYU | Performs the Mann-Whitney U rank test on two independent samples using scipy.stats.mannwhitneyu. |
| MEDIAN_TEST | Performs Mood’s median test to determine if two or more independent samples come from populations with the same median. |
| MOOD | Perform Mood’s two-sample test for scale parameters. |
| POISSON_MEANS_TEST | Performs the Poisson means test (E-test) to compare the means of two Poisson distributions. |
| RANKSUMS | Computes the Wilcoxon rank-sum statistic and p-value for two independent samples. |
| TTEST_IND | Performs the independent two-sample t-test for the means of two groups. |
| TTEST_IND_STATS | Perform a t-test for means of two independent samples using summary statistics. |