P-Value Calculator
Calculate p-value from z-score or t-test to determine statistical significance.
Enter values to see the result.
Guide: P-Value and Statistical Significance
P-value is the probability of obtaining results at least as extreme as the observed ones, assuming the null hypothesis is true. In other words, it measures how much the data contradicts the hypothesis that there is no effect or difference.
Interpreting p-values: A low p-value (typically below 0.05) means the result is unlikely if the null hypothesis were true. This leads to rejecting the null hypothesis and accepting the alternative. A high p-value (above 0.05) means we do not have enough evidence to reject the null hypothesis.
Z-test vs t-test: Z-test is used when the population standard deviation is known or when we have a large sample (n greater than 30). T-test is used when the population standard deviation is unknown and we are working with a smaller sample. T-test is more conservative with small samples.
Statistical vs. practical significance: Statistical significance does not always mean practical significance. With large samples, even small effects can be statistically significant. For example, a 0.01 difference in average price may be statistically significant with a million observations, but it would not have practical importance.
Limitations: P-value does not measure the size of the effect or the probability that the null hypothesis is true. You also cannot compare p-values across different experiments without context. Always report effect size and confidence intervals along with p-values.