Hypothesis Testing

Understand hypothesis testing the way it actually works — not as a formula to memorize, but as a clear line of reasoning you can see.

Hypothesis Testing is a compact, fully offline app that turns the core ideas of statistical inference into interactive diagrams. Whether you are a student meeting p-values for the first time, revising before an exam, or a professional who wants a clean visual reference, this app makes the logic click.

WHY IT'S DIFFERENT
Most resources just throw equations at you. This app shows you the two distributions, shades the errors, and lets you move the sliders — so you can watch how every choice (your significance level, your sample size, the size of the real effect) changes the outcome.

WHAT'S INSIDE

• Concept — The whole framework explained simply with the courtroom analogy: the null hypothesis is "innocent until proven guilty." Covers H₀ vs H₁, the test statistic and p-value, the decision rule, Type I and Type II errors, statistical power, and one- vs two-tailed tests. Clean, offline formulas.

• Decide — Enter a test statistic, choose a tail and your significance level α (preset or custom), and instantly get the p-value, the critical value(s), and a clear Reject / Fail-to-reject verdict — with the null curve and rejection region drawn for you.

• Errors & Power — The signature diagram: two bell curves (H₀ and H₁) side by side, with α (red), β (amber) and power (emerald) shaded. Drag the sliders for effect size, sample size, α and tail, and watch the trade-off between the two kinds of error in real time.

• Simulate — Run the same experiment hundreds of times and watch the rejection rate settle near α when H₀ is true, or near the power when H₁ is true. The clearest way to feel what these numbers really mean.

• Examples — Everyday situations framed as hypothesis tests: a courtroom trial, a new medicine, a smoke alarm. See how the same logic shows up everywhere.

KEY CONCEPTS YOU'LL MASTER
• Null and alternative hypotheses (H₀, H₁)
• p-values and statistical significance
• Critical values and rejection regions
• Type I errors (α) and Type II errors (β)
• Statistical power (1 − β) and effect size
• One-tailed vs two-tailed tests
• How sample size drives everything

BUILT TO BE SIMPLE
• 100% offline — no account, no internet required
• Fast and lightweight — diagrams drawn natively, tiny download
• Clean, distraction-free "Wine Emerald" design

Perfect for students of statistics, psychology, biology, business, data science and the social sciences — and for anyone who has ever wondered what a p-value actually means.

Stop memorizing. Start seeing how hypothesis testing works.