What does a p-value measure in modeling?

The p-value is a statistical measure that helps to determine the strength of evidence against the null hypothesis in hypothesis testing. Specifically, it quantifies the probability of observing data as extreme as, or more extreme than, the observed results, assuming that the null hypothesis is true. A low p-value suggests that the observed effect is unlikely to have occurred under the null hypothesis, leading researchers to consider rejecting the null hypothesis in favor of the alternative hypothesis.

This assessment is fundamental in statistical modeling, as it allows researchers to evaluate whether their findings are statistically significant and not likely due to random chance. A small p-value typically indicates strong evidence against the null hypothesis, prompting further consideration of the alternative hypothesis. By contrast, the other options do not accurately describe the role of the p-value in hypothesis testing. For example, it does not directly measure the accuracy of model predictions or signify the null hypothesis's truth. Instead, it assesses how well the observed data supports or contradicts the null hypothesis.