What does the ‘p’ value in statistics typically indicate?

The 'p' value in statistics is primarily understood as a measure that helps determine the significance of the results obtained from a hypothesis test. Specifically, it represents the probability of observing the data, or something more extreme, assuming that the null hypothesis is true. When researchers conduct a statistical test, they often use the 'p' value to evaluate whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

A smaller 'p' value indicates that the observed data are less likely under the assumption of the null hypothesis, suggesting that there is strong evidence against it. Typically, researchers set a threshold (commonly 0.05) to determine statistical significance, meaning if the 'p' value is less than this threshold, they reject the null hypothesis.

While the significance of outcomes is a related concept, the 'p' value itself directly quantifies the probability related to the null hypothesis rather than the significance in general terms. The strength of correlation refers to the relationship between variables, and reliability pertains to the consistency of a measurement, neither of which are directly indicated by the 'p' value.