Run z-tests, t-tests and chi-square tests with p-values and conclusions.
A hypothesis testing calculator performs one-sample and two-sample t-tests, z-tests, chi-square tests, and ANOVA. Hypothesis testing is the framework for deciding whether observed differences in data are likely due to a real effect or just random sampling variation.
Set a null hypothesis (no effect), an alternative hypothesis (there is an effect), and a significance level (usually α = 0.05). Calculate a test statistic and its p-value. If p < α, reject the null. Critically, "fail to reject" is not the same as "accept" — it just means you don't have enough evidence either way.
t = \frac{\bar{x} - \mu_0}{s / \sqrt{n}}
Inputs: Battery life: claimed mean 100 h. Sample of 16 batteries: mean 95 h, sd 8 h.
The probability of observing data as extreme or more extreme than what you actually saw, assuming the null hypothesis is true. A p-value of 0.03 means: if the null is true, only 3% of similar experiments would produce data this far from the null. Lower p-value = stronger evidence against the null. Note: p-value is NOT the probability that the null is true — a common but serious misinterpretation.
Statistical significance (p < 0.05) means the effect is unlikely due to chance. Practical significance asks whether the effect size is large enough to matter. With huge samples, you can get statistically significant differences that are too small to be useful (e.g. a 0.01% improvement in conversion). Always report and interpret the effect size alongside the p-value.
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