What does the p-value assess in hypothesis testing?

The p-value is a crucial component in hypothesis testing, as it quantifies the evidence against the null hypothesis. Specifically, it assesses the likelihood that the observed results, or more extreme results, would occur by random chance if the null hypothesis were true. A lower p-value indicates stronger evidence against the null hypothesis, suggesting that the observed data is unlikely to have happened under that assumption.

This understanding is fundamental when deciding whether to "reject" or "fail to reject" the null hypothesis based on the threshold set for statistical significance, often at a level of 0.05. When the p-value is less than this significance level, it implies that the results are statistically significant and not likely a product of random variability. Hence, option B accurately describes the role of the p-value in hypothesis testing.