F-test
Sign in to savethumb|An F-test pdf with d1 and d2 = 10, at a significance level of 0.05. (Red shaded region indicates the critical region) An F-test is a statistical test that compares variances. It is used to determine if the variances of two samples, or if the ratios of variances among multiple samples, are significantly different. The test calculates a statistic, represented by the random variable F, and checks if it follows an F-distribution. This check is valid if the null hypothesis is true and standard assumptions about the errors (ε) in the data hold.
Article
12 sectionsContents
- Common examples
- ''F''-test of the equality of two variances
- Formula and calculation
- One-way analysis of variance
- Advantages
- Disadvantages
- Multiple-comparison ANOVA problems
- Regression problems
- See also
- References
- Further reading
- External links
thumb|An F-test pdf with d1 and d2 = 10, at a significance level of 0.05. (Red shaded region indicates the critical region) An F-test is a statistical test that compares variances. It is used to determine if the variances of two samples, or if the ratios of variances among multiple samples, are significantly different. The test calculates a statistic, represented by the random variable F, and checks if it follows an F-distribution. This check is valid if the null hypothesis is true and standard assumptions about the errors (ε) in the data hold.
F-tests are frequently used to compare different statistical models and find the one that best describes the population the data came from. When models are created using the least squares method, the resulting F-tests are often called "exact" F-tests. The F-statistic was developed by Ronald Fisher in the 1920s as the variance ratio and was later named in his honor by George W. Snedecor.