Built and fact-checked by the DocNectar team — see our editorial standards
Key Features
Instant Calculation
Get accurate results in real time with our optimized algorithm.
Mobile Friendly
Fully responsive design. Works on all devices & screen sizes.
Privacy Focused
Your data stays on your device. We don't store any inputs.
100% Free
No hidden costs. This tool is completely free forever.
A p-value measures how likely your observed test statistic (or something more extreme) would be if there were truly no effect — and finding it requires the right distribution formula depending on whether you're working with a z-score or a t-statistic.
This calculator finds the p-value for a z or t test statistic, for one-tailed or two-tailed tests.
How it works
Enter your test statistic, choose z-test or t-test (with degrees of freedom for a t-test), and choose one-tailed or two-tailed. For a z-test, the calculator uses the standard normal distribution's cumulative probability. For a t-test, it uses the mathematical relationship between the Student's t-distribution and the regularized incomplete beta function.
- Enter test statistic (z or t).
- Enter test type.
- Enter degrees of freedom (t-test only).
- Enter tails.
- Click Calculate to see your results.
Examples
t = 2.228, df = 10, two-tailed
A t-statistic of 2.228 with 10 degrees of freedom, two-tailed, gives a p-value of almost exactly 0.05 — this is the classic textbook critical value for that significance level.
Who should use it
- Finding the p-value for a hypothesis test in a statistics course.
- Checking a p-value from a research paper or statistical software output.
Industry applications
- Statistics and research methodology education
- Scientific research and data analysis
Advantages
- Supports both z-tests and t-tests from a single calculator.
- Uses the exact mathematical relationship to the incomplete beta function for t-tests, not a rough table lookup.
Limitations
- Requires you to already have a computed test statistic — it doesn't calculate the test statistic itself from raw sample data.
Common mistakes to avoid
- Using a two-tailed p-value when your hypothesis was actually specifically directional (one-tailed), or vice versa — this changes the p-value by a factor of 2.
- Forgetting to enter degrees of freedom for a t-test, which is required for the t-distribution formula.
Best practices
- Decide whether your test is one-tailed or two-tailed before looking at your data, not after — choosing based on which gives a "better" result is a form of bias.
Tips
- As degrees of freedom get very large, the t-distribution approaches the normal distribution — so a t-test and z-test with the same statistic will give nearly identical p-values for large samples.