What does a p-value indicate in hypothesis testing?

A p-value is a statistical measure that helps researchers determine the significance of their results in the context of hypothesis testing. Specifically, it indicates the probability of obtaining results that are at least as extreme as those observed in the study, assuming that the null hypothesis is true.

In other words, the p-value assesses how well the sample data fits the null hypothesis, which posits that there is no effect or no difference. A low p-value suggests that the observed data would be very unlikely if the null hypothesis were true, leading researchers to consider rejecting the null hypothesis in favor of the alternative hypothesis, which posits that there is some effect or difference.

Understanding the p-value is crucial for interpreting the outcomes of research, as it quantifies the weight of evidence against the null hypothesis and informs conclusions about the effectiveness or impact of a treatment, intervention, or other factors under study. This is why the correct option focuses on the relationship between the p-value and the null hypothesis, as it captures the essence of what the p-value is measuring in hypothesis testing.