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

The p-value plays a critical role in hypothesis testing as it quantifies the evidence against the null hypothesis. Specifically, it represents the probability of obtaining the observed results, or more extreme results, given that the null hypothesis is true. When conducting a hypothesis test, researchers typically set a significance level (often 0.05 or 0.01) to compare the p-value against this threshold.

If the p-value is less than or equal to the predetermined significance level, it suggests that the observed data is unlikely under the null hypothesis, leading researchers to reject the null hypothesis in favor of an alternative hypothesis. This decision is pivotal in determining whether there is sufficient evidence to support a specific claim or effect.

Understanding the p-value's role is essential as it highlights how statistical significance is assessed during hypothesis testing, influencing many conclusions drawn in research studies. Choosing an appropriate significance level and interpreting the p-value effectively can greatly impact the interpretation of statistical results.