0.000017
0.999983
0.000034
1.055009
0.000017
0.999983
0.000034
1.055009
This F-Score P-Value Calculator converts an F statistic into a p-value using a fast approximation. F-tests are commonly used in ANOVA, comparing variances, and testing whether groups or model terms explain a meaningful amount of variation.
Enter your F-score plus numerator and denominator degrees of freedom (dfn, dfd). The calculator returns the right-tailed p-value (the most common for F-tests), along with left-tailed and two-tailed values for reference.
For most F-tests, the p-value is computed as a right-tail probability:
This calculator uses a practical approximation based on the idea that log(F) can be treated as approximately normal for many df values. It estimates an equivalent z-score and then converts that z-score into tail probabilities using a standard normal CDF approximation.
The right-tailed p-value is typically the one reported in ANOVA and most variance-related F-tests. A smaller p-value suggests the observed F statistic would be less likely under the null hypothesis.
Many U.S. use cases compare p-values to 0.05, but practical importance and effect size still matter.
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A moderate F-score. The right-tail p-value is usually not extremely small in this range.
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A larger F-score typically pushes further into the right tail, reducing the p-value.
In most F-tests (including ANOVA), you use the right-tailed p-value: the probability of getting an F statistic at least as large as the observed value under the null hypothesis.
dfn is the numerator degrees of freedom, and dfd is the denominator degrees of freedom. In ANOVA, these depend on the number of groups and sample sizes; in regression, they relate to model terms and residual degrees of freedom.
Yes. Depending on how the test is set up, F can be below 1. Many common reporting conventions focus on right-tail probabilities, but the exact interpretation depends on the test design.
This version uses a fast approximation. Exact matching requires the F-distribution CDF (regularized incomplete beta function) in the backend math engine.
Not always. P-values are influenced by sample size and variability. Use effect size metrics and domain context alongside statistical significance.
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