## What does a P value mean?

In technical terms, a P value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis.

For example, suppose that a vaccine study produced a P value of 0.04..

## What does a high P value tell you?

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. … A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis.

## What does P value of 0.05 mean?

P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

## What does the P value not tell you?

A p-value can tell you that a difference is statistically significant, but it tells you nothing about the size or magnitude of the difference. “The p-value is low, so the alternative hypothesis is true.” … If you use an alpha level of 0.05, there’s a 5% chance you will incorrectly reject the null hypothesis.

## How does P value work?

A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.

## Why is p value important?

The P value means the probability, for a given statistical model that, when the null hypothesis is true, the statistical summary would be equal to or more extreme than the actual observed results [2]. … The smaller the P value, the greater statistical incompatibility of the data with the null hypothesis.