Critical Value Calculator
Look up critical values for the most common statistical distributions: z (standard normal), Student's t, chi-square, and F. Enter your significance level and degrees of freedom to find the threshold for hypothesis testing.
Critical values are the boundary points that define rejection regions in hypothesis testing. If your test statistic exceeds the critical value, you reject the null hypothesis at the chosen significance level.
Z critical values are used for large-sample tests and proportions. Common values: 1.645 (alpha=0.05, one-tailed), 1.960 (alpha=0.05, two-tailed), 2.576 (alpha=0.01, two-tailed). T critical values are used for small-sample means and depend on degrees of freedom; they approach z values as df increases. Chi-square critical values are used for goodness-of-fit and independence tests. F critical values are used for ANOVA and comparing variances.
One-tailed vs two-tailed: One-tailed tests use the full significance level in one direction. Two-tailed tests split alpha equally between both tails. Use two-tailed when you want to detect differences in either direction.