p-value Calculator

Calculate p-values from test statistics for Z, t, chi-square, and F distributions

Calculate p-value from Test Statistics

Choose the distribution of your test statistic

The computed test statistic from your data

Type of alternative hypothesis

Threshold for statistical significance

p-value Results

p = 0.049996
p-value
Significant
at α = 0.05

Test Information

Distribution: Standard Normal Distribution (Z-test)
Test Statistic: 1.96
Alternative Hypothesis: two tailed
Formula: p = 2 × Φ(-|z|)

Interpretation

Moderate evidence against the null hypothesis

Decision: Reject the null hypothesis H₀ at the 5% significance level.

Step-by-Step Example

Z-test Example

Problem: Testing if population mean differs from 100

Sample: n=36, x̄=97, σ=12

Null hypothesis: H₀: μ = 100

Alternative: H₁: μ ≠ 100 (two-tailed)

Solution Steps

1. Calculate Z-score: Z = (x̄ - μ₀)/(σ/√n) = (97-100)/(12/√36) = -1.5

2. For two-tailed test: p-value = 2 × Φ(-|Z|) = 2 × Φ(-1.5)

3. p-value = 2 × 0.0668 = 0.1336

4. Since p = 0.1336 > α = 0.05, fail to reject H₀

p-value Interpretation

p ≤ 0.001
Highly significant
Strong evidence against H₀
0.001 < p ≤ 0.01
Very significant
Strong evidence against H₀
0.01 < p ≤ 0.05
Significant
Moderate evidence against H₀
p > 0.05
Not significant
Insufficient evidence against H₀

Common Test Types

Z-test

• Large samples (n ≥ 30)

• Known population σ

• Population means

t-test

• Small samples or unknown σ

• df = n - 1 (one sample)

• Population means

Chi-square

• Goodness of fit tests

• Independence tests

• Variance tests

F-test

• ANOVA tests

• Variance equality

• Regression significance

Quick Tips

p-value measures evidence against null hypothesis

Lower p-values indicate stronger evidence

Choose significance level before testing

Consider practical significance too

Understanding p-values

What is a p-value?

The p-value is the probability that the test statistic would produce values at least as extreme as the observed value, assuming the null hypothesis is true. It measures the strength of evidence against the null hypothesis.

How to Interpret

  • Small p-value: Strong evidence against H₀
  • Large p-value: Weak evidence against H₀
  • p ≤ α: Reject null hypothesis
  • p > α: Fail to reject null hypothesis

Calculation Methods

p-values are calculated using cumulative distribution functions (CDFs) of the appropriate statistical distribution. The formula depends on whether you're conducting a one-tailed or two-tailed test.

Common Formulas

Left-tailed: p = CDF(test statistic)
Right-tailed: p = 1 - CDF(test statistic)
Two-tailed: p = 2 × CDF(-|test statistic|)