A p-value is the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is true for the populations. P-values are calculated based on your sample data and under the assumption that the null hypothesis is true. Lower p-values indicate greater evidence against the null hypothesis.
Use p-values during hypothesis testing to help you determine which hypothesis the data support. Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant. In other words, the evidence in your sample is strong enough to be able to reject the null hypothesis at the population level.
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