What does the p-value indicate in statistical testing?

The p-value is a fundamental concept in statistical testing, indicating the significance of observed results in relation to a null hypothesis (H0). Specifically, it quantifies the probability of obtaining results at least as extreme as those observed, assuming that the null hypothesis is true. A low p-value suggests that such extreme results would be unlikely under the null hypothesis, thus providing evidence to reject H0 in favor of an alternative hypothesis.

Understanding the p-value is crucial for researchers, as it helps in determining whether the findings of a study are statistically significant or if they may have occurred due to random chance. Typically, a threshold (often set at 0.05) is used; if the p-value falls below this threshold, the results are deemed statistically significant, indicating that the observed effect is unlikely to be due to random variation alone. This makes the p-value a critical tool in evaluating research outcomes and making informed conclusions in the field of healthcare research and beyond.