Home›Math & Stats›F critical value

F Critical Value Calculator

Your test setup

Need a level that is not listed? Choose “Custom” and type any significance level below.

In a one-way ANOVA with k groups and N observations, df1 = k − 1 and df2 = N − k.

The F-test is one-tailed: only large F values count against the null hypothesis, so the whole significance level sits in the upper tail.

F critical value (F*)
3.098
α = 0.05 · df1 = 3 · df2 = 20
Significance level (α)0.05
Numerator df (df1)3
Denominator df (df2)20
Upper-tail area above F*5.0%
Mean of the F-distribution1.111
The calculation
P(F > 3.098) = 0.05, df1 = 3, df2 = 20
F* = 3.098

With 3 and 20 degrees of freedom, an F statistic above 3.098 falls in the top 5% of the F-distribution, so it is significant at the 5% level. The mean of this F-distribution is 1.111.

Example: α = 0.05, df1 = 3, df2 = 20 — F* = 3.098.

Behind the numbers

How the F critical value is calculated

The F critical value F* is the point on the F-distribution with the chosen degrees of freedom that leaves the significance level α in the upper tail. In other words, it is the (1 − α) quantile of the F-distribution: the value where the cumulative distribution function equals 1 − α. This calculator finds it exactly by inverting the F cumulative distribution function, which is built from the regularized incomplete beta function — no rounded table lookup, and it works for any degrees of freedom up to 120 and beyond.

The formula
F* = F&supminus;¹ (1 − α; df1, df2)

where F is the CDF of the F-distribution with df1 numerator and df2 denominator degrees of freedom. Worked example: α = 0.05, df1 = 3, df2 = 20 means F* is the 95th percentile of the F-distribution with 3 and 20 degrees of freedom: F* = 3.098. The mean of that F-distribution is df2 ÷ (df2 − 2) = 20 ÷ 18 = 1.111.

Worked examples

Example F critical values

Significance (α)df1df2F*
0.053203.098
0.051104.965
0.012305.390
0.055103.326
0.104122.480
0.0521203.072

As the denominator degrees of freedom grow, F* falls toward 1: with df1 = 2 and df2 = 120 the 5% critical value is 3.072, and with df2 = 1,000 it is 3.005. Smaller significance levels give larger critical values, because less of the distribution is allowed in the upper tail.

Using F* the right way

01

In a one-way ANOVA, df1 = number of groups − 1 and df2 = total observations − number of groups. Four groups with 24 observations give df1 = 3 and df2 = 20, so the 5% critical value is 3.098.

02

Compare your F statistic with F*: if F is larger than F*, the result is significant at your chosen level. An F of 4.20 with F* = 3.098 is significant at 5%; an F of 2.50 is not.

03

Pick the significance level before you run the test. Switching from 0.05 to 0.10 after seeing the result just to reach significance is not honest statistics.

04

Do not round F* early. A rounded F* of 3.10 instead of 3.098 is harmless, but rounding to 3.0 can flip a borderline decision, so keep three decimals until the final comparison.

Good to know

Your F critical value questions, answered.

What is an F critical value?

An F critical value, written F*, is the cutoff on the F-distribution that leaves the chosen significance level in the upper tail. For example, with 3 numerator and 20 denominator degrees of freedom, the 5% F critical value is 3.098: only 5% of the F-distribution with those degrees of freedom lies above 3.098.

When do I use an F critical value?

Use an F critical value in an F-test, most often a one-way ANOVA that compares the means of three or more groups, or a test that compares two variances. If your calculated F statistic is larger than F*, you reject the null hypothesis at the chosen significance level.

What are numerator and denominator degrees of freedom?

In a one-way ANOVA with k groups and N total observations, the numerator degrees of freedom are k − 1 and the denominator degrees of freedom are N − k. For example, 4 groups with 24 observations give df1 = 3 and df2 = 20, and the 5% F critical value is 3.098.

How is the F critical value calculated?

This calculator inverts the F cumulative distribution function exactly, using the regularized incomplete beta function, instead of reading a rounded table. It works for any degrees of freedom from 1 to 120 and beyond, for significance levels of 0.10, 0.05, 0.025 and 0.01, or any custom level you type.