P-Value Calculator with Tail Selection
Convert a test statistic to a p-value for the normal, Student t, chi-square or F distribution while showing degrees of freedom and the selected tail definition.
Distribution, statistic and tail
Calculated p-value
p-value calculator: method, formulas and interpretation
The p-value calculator uses a dedicated calculation path for this exact problem rather than a generic input-and-output shell. Inputs are normalized before the formula is applied, and the result panel exposes the assumptions needed to audit the answer.
Normal, t, chi-square and F distributions
The p-value calculator evaluates normal and Student t statistics with left, right or two-sided tails. Chi-square and F statistics use nonnegative right-tail probabilities because their standard tests are usually defined in the upper tail. Degrees of freedom are required where the reference distribution depends on them.
Statistical interpretation
The p-value calculator assumes the entered statistic and degrees of freedom came from a valid test whose assumptions were checked. Multiple testing, optional stopping, model selection and poor measurement can make a small p-value misleading. Report effect estimates and confidence intervals with the p-value, and follow the tail definition specified before viewing the data.
Worked use and validation
The p-value calculator rejects missing, non-finite or physically impossible values with a specific message. Load the worked example to inspect the complete calculation and compare it with a manual result before relying on a planning estimate.
How to read the output
The p-value calculator separates the primary result from supporting quantities, unit conversions and limitations. Display rounding does not replace the unrounded values used inside later steps. Use consistent inputs and preserve the stated model when comparing scenarios.
Numerical distribution evaluation
The p-value calculator evaluates normal tails directly and uses beta- or gamma-function relationships for Student t, chi-square and F distributions. This avoids a short lookup table and permits non-integer degrees of freedom where the distribution supports them. Extremely small probabilities should still be reported with sensible precision rather than as exact zero.
Can a p-value prove the null hypothesis?
No. It describes tail probability under the specified null model.
Why are chi-square and F tests right-tailed here?
Their common test statistics become more extreme as they increase.
What does two-sided mean?
For symmetric z and t distributions, it doubles the smaller one-sided tail probability.