What is the significance of the p-value in hypothesis testing?

The p-value in hypothesis testing plays a crucial role in determining the strength of the evidence against the null hypothesis. Specifically, it indicates the probability of observing the results—or something more extreme—given that the null hypothesis is true. This means that a low p-value suggests that the observed data would be very unlikely if the null hypothesis were correct, leading researchers to consider rejecting the null hypothesis in favor of the alternative hypothesis.

Understanding the p-value in this context is essential for interpreting statistical results. It helps researchers assess whether the data collected provide sufficient evidence to conclude that an effect or relationship exists, based on a predefined significance level, commonly set at 0.05. If the p-value is less than this threshold, it generally suggests that the results are statistically significant, warranting further investigation or action.

The other options do not accurately describe the role of the p-value. While the p-value does not indicate the strength of the alternative hypothesis directly, nor does it show the likelihood of the null hypothesis being true or measure effect size, it is fundamentally about assessing the probability of the observed evidence under the assumption that the null hypothesis holds true.