When should I use a one-tailed vs two-tailed test?

Use a one-tailed (directional) test when you're only interested in detecting an effect in one direction (e.g., is the mean greater than X?). Use a two-tailed test when detecting any difference from the null hypothesis (e.g., is the mean different from X?). Two-tailed tests are more conservative and generally preferred.

What's the difference between z-test and t-test?

Use z-test when you know the population standard deviation and have a large sample (n > 30). Use t-test when the population standard deviation is unknown and you're using the sample standard deviation. The t-distribution has heavier tails, making it more appropriate for small samples.

How do I interpret a very small p-value?

Very small p-values (like p < 0.001) indicate strong evidence against the null hypothesis. Common conventions: p < 0.05 (significant), p < 0.01 (highly significant), p < 0.001 (very highly significant). However, statistical significance doesn't always mean practical significance.

Statistics

P-Value Calculator

Calculate p-value for statistical hypothesis testing. Supports z-test, t-test, chi-square test, and F-test with one-tailed and two-tailed options.

Test Type

Tail Type

Test Statistic

Made with love

Support

Related Calculators

You might also find these calculators useful

Z-Score Calculator

Calculate z-score, percentile, and probability

Standard Deviation Calculator

Calculate standard deviation and variance

Variance Calculator

Calculate variance and analyze data spread

Weighted Average Calculator

Calculate weighted mean with custom weights

P-Value Calculator for Statistical Testing

Calculate p-values for hypothesis testing using z-scores, t-statistics, chi-square values, or F-statistics. Get accurate statistical significance with support for one-tailed and two-tailed tests. Essential for research, data analysis, and academic studies.

What is a P-Value?

A p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis. In most research, p-values less than 0.05 are considered statistically significant.

P-Value Interpretation

p < α → Reject H₀

How to Use This Calculator

P-Value Applications

Medical Research

Test drug efficacy and treatment outcomes.

A/B Testing

Determine if website changes improve conversion rates.

Quality Control

Verify manufacturing processes meet specifications.

Academic Research

Validate hypotheses in social sciences, psychology, and economics.

Frequently Asked Questions

A p-value of 0.05 means there's a 5% probability of observing your results (or more extreme) if the null hypothesis were true. At the conventional α = 0.05 significance level, this is borderline significant—results are often reported as 'marginally significant' at this threshold.