StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Cochran's Q Test
Cochran's Q Test compares proportions across three or more related groups. Use it when the same subjects are measured under multiple conditions and the outcome is binary (success/failure).
Two-Way ANOVA
Two-Way ANOVA tests the effect of two independent factors (and their interaction) on a continuous outcome. Use it when you have two categorical grouping variables and want to know if either or both affect your outcome.
One-Way ANCOVA
The One-Way ANCOVA is a test used to determine if 3+ groups are different from each other on your variable of interest in the presence of a covariate.
Factorial ANOVA
The Factorial ANOVA is a test used to determine if two or more sets of groups are significantly different from each other on your variable of interest.
Split Plot ANOVA
The Split Plot ANOVA is a statistical test used to determine if 2 or more repeated measures from 2 or more groups are significantly different from each other on your variable of interest.
Friedman Test
The Friedman Test is a test used to determine if 3 or more measurements from the same group are different from each other on a skewed variable of interest.
Kruskal-Wallis One-Way ANOVA
The Kruskal-Wallis One-Way ANOVA is a test used to determine if 3+ groups are different from each other on a skewed variable of interest.
One-Way Repeated Measures ANOVA
One-Way Repeated Measures ANOVA is a test used to determine if 3+ related groups are significantly different from each other on a variable of interest.
One-Way ANOVA
The One-Way ANOVA is a statistical test used to determine if 3 or more groups are significantly different from each other on a variable of interest.
Mann-Whitney U Test
The Mann-Whitney U Test compares two different groups on your variable of interest (dependent variable) when your variable of interest is skewed.
Single Sample Wilcoxon Signed-Rank Test
The Single Sample Wilcoxon Signed-Rank Test is a statistical test used to see if a single group is significantly different from a known population value.
