Hypothesis Testing & ANOVA
Business-stats essentials: one-sample t-test, two-sample t-test, and one-way ANOVA with critical values, approximate p-values, and clear reject / fail decisions.
Configure test
Hypothesis testing mindset
State H₀ (status quo) and Hₐ, pick significance α, compute a test statistic, and decide reject / fail to reject using a critical value or p-value.
One-sample t
Use when the population stddev is unknown and data are roughly normal (or n is moderate/large).
Two-sample t
Compares means of two independent samples. This tool uses pooled variance by default (equal-variance assumption).
One-way ANOVA
Tests whether k group means are all equal. A significant F means at least one group differs — not which one (use post-hoc tests outside this tool).
p-value note
p-values here use numerical approximations of the t and F distributions — accurate enough for coursework and screening decisions.
Pre-loaded samples match common textbook exercises. Switch test type, then load example.