Critical Value Calculator

Choose the distribution, significance level and tail to find the critical value.

How it is calculated

The tool finds the point where the tail area equals your significance level α (a number strictly between 0 and 1, such as 0.05). With a two-tailed test, α/2 goes in each tail; with a one-tailed test the whole α sits in one tail. For z the cutoff is the normal quantile for that tail area. For Student's t, the tail probability is computed from the exact t distribution with df degrees of freedom and the cutoff is found by bisection, so it matches a printed table to four decimals (for example 12.7062 at df = 1, two-tailed, α = 0.05). Chi-square always uses the right tail and the full α, so the tail choice has no effect; for t and chi-square a missing or non-positive df gives no result. The value is shown with four decimals.

Worked example

You run a two-tailed t test at α = 0.05 with 10 degrees of freedom. The two tails share the 5%, so 0.025 sits in each tail and the tool looks for the point with 2.5% of the distribution above it. The normal-distribution value for that is 1.96, and the heavier tails of the t distribution at 10 df push it up to 2.2281. A test statistic above 2.2281 or below −2.2281 lets you reject the null hypothesis at the 5% level.

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Common mistakes

  • Entering 5 instead of 0.05 for a 5% level. Alpha must be a proportion between 0 and 1, otherwise the tool shows no result.
  • Choosing one-tailed when the test is two-tailed. At α = 0.05 the z cutoff is 1.6449 one-tailed but 1.9600 two-tailed, which can change the decision.
  • Using the sample size as the degrees of freedom. For a one-sample t test df is n − 1, not n.
  • Expecting the tail option to change a chi-square result. At α = 0.05 and df = 5 it returns 11.0705 whichever tail is selected.
  • Leaving the degrees of freedom empty for t or chi-square. Only z works without df.

About this calculator

A critical value is the cutoff point on a probability distribution — the z, Student's t, or chi-square distribution — beyond which a test statistic is considered extreme enough to reject the null hypothesis. It depends on the chosen significance level (α), such as 0.05, and on whether the test is one-tailed or two-tailed: a two-tailed test at α = 0.05 splits the 5% between both ends of the distribution, giving a different cutoff than a one-tailed test at the same α.

Researchers, statisticians, and students look up critical values to decide whether a computed test statistic is statistically significant, and to construct confidence intervals — the t-distribution is used instead of z when the sample size is small and the population standard deviation is unknown, while chi-square critical values are used for variance tests and goodness-of-fit tests.

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