P-value Calculator
Online P-value Calculator
📊 The p-value is the probability of obtaining a result at least as extreme as the observed one, assuming the null hypothesis is true. This calculator helps determine the statistical significance of your results.
🔬 What is a p-value?
The p-value measures the strength of evidence against the null hypothesis. The smaller the p-value, the stronger the evidence against the null hypothesis:
- p < 0.001: Very strong evidence
- p < 0.01: Strong evidence
- p < 0.05: Moderate evidence (traditional threshold)
- p ≥ 0.05: Weak or no evidence
📈 Types of statistical tests:
- T-test: Comparing means
- Z-test: For large samples (n > 30)
- Chi-square: Independence test for categorical variables
- F-test: Comparing variances
🎯 Applications:
- Scientific research and experiments
- Medical trials
- A/B testing in marketing
- Quality control
- Academic research
🎓 The calculator is suitable for students, scientists, analysts, and anyone working with statistical data.
Frequently Asked Questions
What does a p-value less than 0.05 mean?
A p-value less than 0.05 means there's less than a 5% chance of getting such results by random chance. This is traditionally considered statistically significant.
Should I always use the 0.05 threshold?
No, the significance threshold depends on context. Medicine often uses 0.01, while some fields use 0.10. It's important to set the threshold before conducting research.
What are one-tailed and two-tailed tests?
A one-tailed test checks the hypothesis in one direction (greater or less), while a two-tailed test checks in both directions (difference in any direction).
How to interpret large p-values?
A large p-value (>0.05) doesn't mean the null hypothesis is correct. It means there's insufficient evidence to reject it.
What is statistical power?
Statistical power is the probability of correctly rejecting a false null hypothesis. It depends on effect size, sample size, and significance level.
Can a p-value be zero?
Theoretically no, but very small p-values may be rounded to zero in practice. In such cases, we write p < 0.001.