Calculate p-values for Z-tests, T-tests, and Chi-square tests — one-tailed or two-tailed. Includes a live distribution curve with the rejection region, significance verdict at α=0.05 and 0.01, and plain-English interpretation.
Select the test type and tail direction, then enter your statistic.
Select the test type, enter your statistic (or raw inputs), and press Calculate to get the exact p-value with distribution curve and significance verdict.
The p-value is the probability of observing results at least as extreme as yours, if the null hypothesis were true. It is not the probability that H₀ is true, nor the probability that the result is due to chance. A small p-value (typically <0.05) means the data would be unlikely under H₀, giving evidence to reject it.
The significance level α (commonly 0.05 or 0.01) is set before collecting data. You reject H₀ when p < α. Choosing α=0.05 means you accept a 5% chance of a false positive (Type I error). A smaller α reduces false positives but increases the risk of missing a real effect (Type II error, power).
In DMAIC Analyse and Improve phases, hypothesis tests determine whether a factor has a statistically significant effect on the output. Common tests: one-sample t (is the mean at target?), two-sample t (do two groups differ?), chi-square (are defect counts independent of category?). P < 0.05 is standard; for critical decisions use α = 0.01.