Calculate p-values from test statistics for hypothesis testing. Choose your test type below.
Enter the Z-score from your statistical test.
Enter the T-statistic and degrees of freedom.
Enter the Chi-square statistic and degrees of freedom.
Enter the F-statistic and both degrees of freedom.
Enter values and click Calculate
The p-value (probability value) is the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true.
The significance level (alpha, α) is the threshold you set before conducting your test to determine whether results are statistically significant.
| Significance Level | Interpretation | Common Use |
|---|---|---|
| α = 0.01 | 99% confidence | Clinical trials, high-stakes decisions |
| α = 0.05 | 95% confidence | Most scientific research (standard) |
| α = 0.10 | 90% confidence | Exploratory research, preliminary studies |
Very strong evidence against null hypothesis. Results are highly unlikely to be due to chance. Reject null hypothesis with high confidence.
Strong evidence against null hypothesis. Standard threshold for statistical significance in most research. Reject null hypothesis.
Weak evidence against null hypothesis. May warrant further investigation but not conclusive. Use caution.
Insufficient evidence against null hypothesis. Results could easily be due to chance. Fail to reject null hypothesis.
If p-value ≤ α:
→ Reject null hypothesis (H₀)
→ Results are statistically significant
→ Evidence supports alternative hypothesis (H₁)
If p-value > α:
→ Fail to reject null hypothesis (H₀)
→ Results are not statistically significant
→ Insufficient evidence for alternative hypothesis
Z = (x̄ - μ) / (σ/√n)
t = (x̄ - μ) / (s/√n)
χ² = Σ((O - E)² / E)
F = variance₁ / variance₂
Scenario: Testing if a new drug reduces blood pressure
Results: After testing, you get p = 0.023
Interpretation:
Since p (0.023) < α (0.05), we reject the null hypothesis. There is only a 2.3% chance these results occurred by random chance. The drug significantly reduces blood pressure at the 95% confidence level.
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One‑tailed tests look for an effect in one direction; two‑tailed tests detect effects in either direction. Match the alternative hypothesis you stated a priori.
It’s the probability, under the null, of observing data at least as extreme as yours. It’s not the probability the null is true.
Use Z for large‑sample or known σ tests; t for small‑sample unknown σ; χ² for variances/contingency; F for variance ratios/ANOVA.
p≠probability the null is true; non‑significant ≠ no effect; significant ≠ practically important.