What does the p-value indicate in hypothesis testing?

The p-value is a crucial concept in hypothesis testing as it quantifies the probability of observing the data, or something more extreme, given that the null hypothesis is true. This means that a p-value provides insight into the compatibility of the observed data with the null hypothesis. A low p-value indicates that the observed results are unlikely under the assumption that the null hypothesis is valid, which may lead researchers to reject the null hypothesis in favor of the alternative hypothesis. This interpretative role of the p-value is fundamental in determining statistical significance.

Considering the other options, the degree of internal validity pertains to how well a study can establish a cause-and-effect relationship, not how data is interpreted in terms of hypothesis testing. The strength of the correlation between variables refers to how closely related two variables are, which is distinct from the p-value's role in hypothesis testing. Lastly, the size of the effect being tested involves the magnitude of a relationship or difference, which is separate from the p-value, focusing instead on statistical significance versus practical significance.