F-Test / F-Distribution Calculator
Compare variances or examine an F statistic in MiniWebtool’s F-Test / F-Distribution Calculator. Modes cover two-sample variance testing, one-way ANOVA with up to 10 groups, and direct F-distribution probability lookups with rejection-region plots.
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Enter two sample variances and sizes (or ANOVA groups, or an F value with its degrees of freedom) below to get F, the p-value and the test conclusion with the steps.
About F-Test / F-Distribution Calculator
The F-Test / F-Distribution Calculator performs F-tests for ANOVA (Analysis of Variance), comparisons of variance between two samples, 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, the classical two-sample F-test for equal variances, and direct p-value lookups for any F-value and degrees of freedom combination.
At a Glance
The F-Test / F-Distribution Calculator runs a one-way ANOVA, a two-sample variance F-test, or a probability lookup for a known F-statistic. It returns the F-statistic, p-value, critical value and a curve with the rejection region marked.
| Feature | What you get | Good for |
|---|---|---|
| One-way ANOVA | Enter comma-separated values for 2 to 10 groups, with at least 2 values in each. Add or remove groups with buttons. | Comparing means across three or more groups |
| Two-sample variance test | Enter two sample variances and two sample sizes to test whether the populations have equal variance. | Checking equal variances before a two-sample t-test |
| Custom F-value | Enter an F-statistic with numerator and denominator degrees of freedom (df₁, df₂) to get its p-value. | Looking up a p-value for a statistic from other software or a paper |
| Significance level | Choose α of 0.10, 0.05, 0.025, 0.01, 0.005 or 0.001 for the hypothesis test conclusion. | Matching the α your course or study specifies |
| Curve and workings | Shows an F-distribution curve with the rejection region highlighted, plus step-by-step workings and critical values. | Seeing where your F-value falls against the critical value |
| Example presets | Buttons fill in sample data such as test scores, drug dosages, plant growth, machine precision and lab measurements. | Trying each mode before entering your own data |
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
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