F-Distribution Calculator

Enter an F-statistic and numerator/denominator degrees of freedom to compute the CDF and p-value instantly.

Use the F-Distribution Calculator

Input Parameters

Results

CDF — P(F ≤ observed) —
p-value — P(F > observed) —
PDF at F —
Mean (d2 > 2) —
Variance (d2 > 4) —
Mode (d1 > 2) —

Key Formulas

  • CDF: I(d1·F/(d1·F+d2), d1/2, d2/2)
  • p-value: 1 − CDF
  • PDF: F^(d1/2−1) · (d2+d1·F)^(−(d1+d2)/2) · K
  • Mean: d2 / (d2 − 2), when d2 > 2
  • Variance: 2d2²(d1+d2−2) / (d1(d2−2)²(d2−4))
  • Mode: (d1−2)/d1 · d2/(d2+2), when d1 > 2

PDF Curve

F(3, 20)

The vertical line marks the observed F-statistic. The shaded area is the p-value (right tail).

Common Presets

Significance Level Guide

α = 0.10
—
Critical F
α = 0.05
—
Critical F
α = 0.01
—
Critical F

Summary

The F-distribution is a continuous probability distribution that arises frequently in ANOVA, regression analysis, and variance-ratio tests. It is parameterized by two positive integers: the numerator degrees of freedom (d1) and denominator degrees of freedom (d2). This calculator computes the cumulative distribution function (CDF), upper-tail p-value, PDF value at the given F-statistic, and key distribution statistics entirely in your browser.

How it works

  1. Enter the observed F-statistic (must be greater than 0).
  2. Enter the numerator degrees of freedom (d1) — typically the number of groups minus 1.
  3. Enter the denominator degrees of freedom (d2) — typically the total observations minus the number of groups.
  4. Click Calculate or edit any field to see the CDF, p-value, PDF, mean, and variance.
  5. Read the p-value to decide statistical significance at your chosen alpha level.
  6. Use the preset buttons to explore common ANOVA and regression scenarios.

Use cases

  • Determine statistical significance in one-way and two-way ANOVA.
  • Evaluate the overall fit of a regression model (F-test for joint significance).
  • Compare variances between two populations with an F-test.
  • Compute p-values for ANOVA output when no software is available.
  • Teach and visualize the shape of the F-distribution for different degrees of freedom.
  • Verify statistical software output during quality control.
  • Assess whether group means differ significantly in experimental research.
  • Evaluate nested model comparisons in regression and mixed models.

Frequently Asked Questions

Last updated: 2026-05-25 · Reviewed by Nham Vu