P-Value Calculator
Calculate p-values for hypothesis testing using z-tests, t-tests, and chi-square tests.
A p-value calculator determines the probability of observing a test statistic as extreme as the one computed, given the null hypothesis is true.
Examples
Z-test
T-test
One-tailed
Frequently Asked Questions
What is a p-value?
When is a result statistically significant?
One-tailed vs two-tailed?
Quick Tips
- •Double-check your inputs — small errors lead to incorrect results.
- •Choose one-tailed tests only when you have a clear directional hypothesis before collecting data.
- •A small p-value does not measure effect size — always consider practical significance alongside statistical significance.
A p-value calculator determines the probability of observing a test statistic as extreme as the one computed, given the null hypothesis is true.
How to Use This Calculator
Enter the test statistic, select z-test or t-test, and choose one-tailed or two-tailed. For t-tests, enter degrees of freedom. The calculator approximates the p-value and checks significance at common alpha levels.
Understanding the Formula
For z-test: p = P(Z > |z|) for one-tail, 2 × P(Z > |z|) for two-tail. For t-test: uses the t-distribution with specified degrees of freedom.
Examples
Z-test
Z = 1.96, two-tailed: p = 2 × P(Z > 1.96) = 2 × 0.025 = 0.05
T-test
t = 2.5, df = 20, two-tailed: p ≈ 0.021 (significant at α = 0.05)
One-tailed
Z = 2.33, one-tailed: p = P(Z > 2.33) ≈ 0.01
Frequently Asked Questions
What is a p-value?
The p-value is the probability of observing a test statistic at least as extreme as the one computed, assuming the null hypothesis is true.
When is a result statistically significant?
A result is significant when the p-value is less than the chosen significance level (alpha). Common levels are 0.05 (5%) and 0.01 (1%).
One-tailed vs two-tailed?
Use one-tailed when you have a directional hypothesis (e.g., greater than). Use two-tailed when testing for any difference (either direction).
Assumptions & Limitations
- Assumes a standard normal distribution for z-tests and a t-distribution for t-tests.
- Assumes exact input values; rounding in inputs propagates to results.
- P-value approximations may differ slightly from exact statistical tables.