A p-value is a critical measure used to assess the strength of evidence against a null hypothesis, which posits that there is no significant effect, difference, or relationship in a given statistical analysis. It quantifies the probability of obtaining data as extreme as or more extreme than the observed results, assuming that the null hypothesis is true. In simpler terms, it tells you how likely it is to observe your data if the null hypothesis were correct.
People typically interpret p-values as follows: If the p-value is very small (typically less than a predefined significance level, denoted as α, like 0.05 or 0.01), it suggests strong evidence against the null hypothesis, leading to its rejection in favor of an alternative hypothesis. Conversely, if the p-value is relatively large, it implies that the observed data aligns reasonably well with the null hypothesis, often resulting in the failure to reject it. It’s crucial to understand that the p-value doesn’t reveal the actual probability of the null hypothesis being true or false; rather, it helps gauge the strength of evidence against the null hypothesis based on the collected data.