One-Sample t-Test

One-Sample t-Test — does your data's average really differ from a claimed value? Type your measurements, set the claim, and get the t statistic, the p-value, a 95% confidence interval and Cohen's d effect size — with a clear t-distribution chart that shows exactly where your result lands. Fully offline. A company says its batteries last 10 hours. You measure a few and get an average of 9.7. Is that close enough to call the claim true, or is the gap too big to be chance? The one-sample t-test answers exactly that: it measures how many standard errors your sample mean sits from the claim, and tells you whether the difference is real or just noise. No sign-up. No internet needed. No data collected. Just clear, instant statistics. WHAT YOU CAN DO • Test — type or paste your numbers and the claimed mean μ₀, pick a one- or two-tailed alternative, and instantly get the sample mean, t, degrees of freedom, p-value, 95% CI and Cohen's d, with a plain verdict. • Chart — see the t-distribution with the rejection zone shaded and your t marker on it, plus the confidence interval against the claim line μ₀ — so significance is visible, not just a number. • Concept — learn the hypotheses, what the t statistic means, how the p-value and α decide the result, and one- vs two-tailed tests — with offline, beautifully typeset formulas that never overlap. • Sim — slide the true average away from the claim and change the sample size with + / - steppered sliders, then watch how a bigger gap or a bigger sample turns the result significant. • Cases — load real-world claims (battery life, coffee temperature and more) with one tap. WHAT YOU GET • The t statistic and its degrees of freedom. • The p-value (one- or two-tailed) and a clear reject / fail-to-reject verdict. • A 95% confidence interval for the true mean. • Cohen's d, so you know if the difference is big in practical terms. • A t-distribution chart and a CI-vs-claim plot. WHY YOU'LL LIKE IT • See where t lands — the rejection zone makes significance obvious. • Effect size too — not just "significant", but how big the difference is. • Fully offline — every statistic, chart and formula works with no connection. • Hands-on — type exact data, or explore with sliders and + / - steppers. • Clean "Crimson & Indigo" design, easy on the eyes. • Privacy-friendly — your data stays on your device. Whether you are a student learning hypothesis testing, a quality engineer checking a target, or anyone testing a claim against real measurements, One-Sample t-Test gives you the full answer — statistic, interval, effect size and picture — in seconds. Download it, enter your data and the claim, and see if the average really differs.