In statistical hypothesis testing, what does the term "p-value" refer to?

The term "p-value" in statistical hypothesis testing refers specifically to the probability of observing the data, or something more extreme, assuming that the null hypothesis is true. This means that the p-value quantifies how compatible the observed data is with the null hypothesis; a low p-value indicates that the observed data is unlikely under the null hypothesis, leading researchers to consider rejecting it. Essentially, it provides a measure for evaluating the strength of the evidence against the null hypothesis.

Understanding this definition is crucial because it helps in making decisions based on the evidence from the sample data. If the p-value is less than the predetermined significance level (often set at 0.05), it is an indication that the null hypothesis may not hold true, suggesting that there may be significant effects or relationships present in the data observed.