Hypothesis Testing Simulator
An interactive demonstration of hypothesis testing — showing how alpha, effect size, sample size, and power interrelate.
What it does
- Displays null and alternative distributions
- Shows significance level (alpha) and rejection region
- Illustrates Type I error, Type II error, and statistical power
- Adjust parameters to see how power changes
Key concepts illustrated
- Type I error (alpha) — rejecting H0 when it's true (false positive)
- Type II error (beta) — failing to reject H0 when H1 is true (false negative)
- Power (1 - beta) — probability of correctly detecting a real effect
- Larger effect sizes increase power
- Larger samples increase power
- Stricter alpha decreases power
This makes power analysis intuitive rather than abstract — excellent for teaching and for understanding the design decisions behind your study.
A UniversalChatBot is available for discussion.