In statistical testing, what does a p-value indicate?

A p-value serves as a critical indicator in statistical testing, specifically reflecting the strength of evidence against the null hypothesis. When researchers conduct hypothesis testing, they start with an assumption (the null hypothesis) that there is no effect or no difference between groups. The p-value quantifies how compatible the observed data is with that null hypothesis.

A smaller p-value suggests that the observed data is less likely to have occurred under the assumption that the null hypothesis is true. Thus, a low p-value indicates strong evidence against the null hypothesis, leading researchers to consider the possibility of alternative hypotheses instead. Conversely, a high p-value implies that the observed data is consistent with the null hypothesis.

Understanding the role of the p-value is essential as it guides decision-making regarding the validity of the null hypothesis in the context of the data being analyzed.