An F-test is a statistical hypothesis test that uses the F-distribution to compare two variances or to test the overall significance of a regression model. It is most commonly used in ANOVA (Analysis of Variance) to determine whether the means of three or more groups are significantly different.

F-Test / F-Distribution Calculator

Calculate F-statistic and p-value for ANOVA, two-sample variance tests, and regression analysis. Get step-by-step solutions with interactive F-distribution curve visualization, critical value tables, and hypothesis test conclusions.

📌 Quick examples:

Group 1

Group 2

Group 3

📌 Quick examples:

Sample 1 Variance (s₁²)

Sample 2 Variance (s₂²)

Sample 1 Size (n₁)

Sample 2 Size (n₂)

📌 Quick examples:

F-Statistic

df₁ (Numerator)

df₂ (Denominator)

Significance Level (α):

Embed F-Test / F-Distribution Calculator Widget

About F-Test / F-Distribution Calculator

The F-Test / F-Distribution Calculator performs F-tests for ANOVA (Analysis of Variance), two-sample variance comparisons, and custom F-statistic lookups. Enter your data to get the F-statistic, p-value, critical values, step-by-step solutions, and an interactive F-distribution curve with the rejection region highlighted. This tool supports one-way ANOVA with up to 10 groups, two-sample variance tests (Levene-type), and direct p-value lookups for any F-value and degrees of freedom combination.

How to Use the F-Test Calculator

Select your calculation mode — choose "One-Way ANOVA" to compare means across groups, "Two-Sample Variance" to test if two populations have equal variance, or "Custom F-Value" to look up a p-value for a known F-statistic and degrees of freedom.
Enter your data — for ANOVA, enter comma-separated values for each group (at least 2 groups with 2+ values each). For variance test, enter the two sample variances (s²) and sample sizes (n). For custom mode, enter the F-statistic and both degrees of freedom.
Set the significance level (α) — common choices are 0.05 (95% confidence), 0.01 (99% confidence), or 0.10 (90% confidence).
Click Calculate — review the F-statistic, p-value, hypothesis test conclusion, step-by-step workings, and the F-distribution curve showing where your F-value falls relative to the critical value.

What Is the F-Test?

The F-test is a statistical hypothesis test in which the test statistic follows an F-distribution under the null hypothesis. It is used primarily for:

ANOVA (Analysis of Variance): Testing whether the means of three or more groups are equal. The F-statistic is the ratio of between-group variance to within-group variance (MSB/MSW).
Comparing Two Variances: Testing whether two populations have equal variance. The F-statistic is the ratio of the larger sample variance to the smaller one.
Regression Analysis: Testing the overall significance of a regression model. The F-statistic measures whether the explained variance is significantly greater than the unexplained variance.

Understanding the F-Distribution

The F-distribution is a continuous probability distribution that arises as the ratio of two independent chi-squared random variables, each divided by their degrees of freedom. Key properties include:

It is always non-negative (F ≥ 0) and right-skewed
It is defined by two parameters: df₁ (numerator degrees of freedom) and df₂ (denominator degrees of freedom)
As both degrees of freedom increase, the distribution approaches a normal distribution
The mean of the distribution is df₂/(df₂ − 2) when df₂ > 2

One-Way ANOVA Explained

One-way Analysis of Variance (ANOVA) tests whether there are statistically significant differences between the means of three or more independent groups. The procedure decomposes total variability into:

SSB (Sum of Squares Between): Measures variation due to differences between group means
SSW (Sum of Squares Within): Measures variation within groups (random error)
F = MSB/MSW: A large F-statistic indicates that between-group variance is much larger than within-group variance, suggesting the group means are not all equal

Assumptions of the F-Test

Independence: Observations are independent within and between groups
Normality: Data within each group is approximately normally distributed
Homogeneity of Variances: Population variances are equal across groups (for ANOVA)

The F-test is fairly robust to violations of normality, especially with larger sample sizes, but is more sensitive to unequal variances when group sizes are unequal.

When to Use F-Test vs. T-Test

Use a t-test when comparing means of exactly two groups. Use an F-test (ANOVA) when comparing three or more groups simultaneously. Running multiple t-tests instead of ANOVA inflates the Type I error rate (the chance of false positives). For two groups, ANOVA and t-test give equivalent results: F = t².

FAQ

What is an F-test?

An F-test is a statistical hypothesis test that uses the F-distribution to compare two variances or to test the overall significance of a model. It is most commonly used in ANOVA to determine whether the means of three or more groups are significantly different from each other.

What is the F-distribution?

The F-distribution is a right-skewed probability distribution defined by two parameters: the numerator degrees of freedom (df₁) and the denominator degrees of freedom (df₂). It arises as the ratio of two independent chi-squared variables divided by their respective degrees of freedom, and it is always non-negative.

How do I interpret the p-value from an F-test?

The p-value is the probability of observing an F-statistic as extreme as (or more extreme than) the calculated value, assuming the null hypothesis is true. If p < α (your significance level, typically 0.05), you reject the null hypothesis and conclude that there is a statistically significant difference.

What is the difference between one-way ANOVA and two-sample F-test?

One-way ANOVA uses the F-test to compare means across three or more groups by analyzing between-group and within-group variance. A two-sample F-test specifically compares the variances of two populations to determine if they are equal, often as a preliminary check before performing a two-sample t-test.

When should I use an F-test versus a t-test?

Use a t-test when comparing the means of exactly two groups. Use an F-test (ANOVA) when comparing means of three or more groups simultaneously. Running multiple pairwise t-tests instead of ANOVA increases the risk of Type I errors. For two groups, the F-test and t-test produce equivalent results, where F equals t squared.

Reference this content, page, or tool as:

"F-Test / F-Distribution Calculator" at https://MiniWebtool.com/f-test-f-distribution-calculator/ from MiniWebtool, https://MiniWebtool.com/

by miniwebtool team. Updated: 2026-04-13

You can also try our AI Math Solver GPT to solve your math problems through natural language question and answer.

Related MiniWebtools:

Adjacency Matrix CalculatorNew

AI Content DetectorNew

ANOVA Calculator

Antilog CalculatorFeatured

Beta Function Calculator

Binomial Probability Distribution Calculator

Chi-Square Test Calculator

Divisibility Test Calculator

Fisher's Exact Test CalculatorNew

Hypergeometric Distribution CalculatorNew

F-Test / F-Distribution Calculator

About F-Test / F-Distribution Calculator

How to Use the F-Test Calculator

What Is the F-Test?

Understanding the F-Distribution

One-Way ANOVA Explained

Assumptions of the F-Test

When to Use F-Test vs. T-Test

FAQ

What is an F-test?

What is the F-distribution?

How do I interpret the p-value from an F-test?

What is the difference between one-way ANOVA and two-sample F-test?

When should I use an F-test versus a t-test?

Related MiniWebtools:

Statistics And Data Analysis:

Top & Updated: