Home
Back
P-value Calculation:
| From: | To: |
The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming the null hypothesis is true. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis.
The calculator computes p-values using statistical distributions:
Where:
Explanation: The calculation depends on the chosen statistical distribution (normal, t, or chi-squared) and whether the test is one-tailed or two-tailed.
Details: P-values are fundamental in hypothesis testing, helping researchers determine statistical significance and make decisions about rejecting or failing to reject null hypotheses.
Tips: Enter your test statistic, select the appropriate distribution, specify degrees of freedom if needed, and choose the tail type. The calculator will compute the corresponding p-value.
Q1: What does p < 0.05 mean?
A: A p-value less than 0.05 suggests that the observed data would be unlikely if the null hypothesis were true, often leading to rejection of the null hypothesis.
Q2: When should I use a one-tailed vs two-tailed test?
A: Use one-tailed when you're only interested in deviations in one direction. Use two-tailed when you want to detect any significant difference, regardless of direction.
Q3: What's the difference between normal and t-distribution?
A: Use normal for large samples (n > 30) or known population variance. Use t-distribution for small samples with unknown variance.
Q4: Can p-values prove a hypothesis?
A: No, p-values only provide evidence against the null hypothesis. They don't prove the alternative hypothesis is true.
Q5: Why is my p-value displayed as 0.000000?
A: This means the p-value is extremely small (less than 0.000001), indicating very strong evidence against the null hypothesis.