What does a significant p-value indicate in hypothesis testing?

A significant p-value indicates that there is enough evidence to reject the null hypothesis. In hypothesis testing, the p-value represents the probability of observing the test results, or something more extreme, if the null hypothesis is true. A low p-value (typically less than a predetermined significance level, such as 0.05) suggests that the observed data is unlikely under the null hypothesis, which leads researchers to consider the alternative hypothesis as a more plausible explanation.

When the p-value is deemed significant, it reflects strong evidence against the null hypothesis, justifying its rejection in favor of the alternative hypothesis. This is a foundational concept in statistical inference, as it allows researchers to draw conclusions about a population based on sample data. In contrast, a non-significant p-value would suggest that there is not enough evidence to reject the null hypothesis, leading researchers to conclude that the null hypothesis may still be a valid explanation for the observed data.