PSClearn6 – Psychology Research Methods Practice Test

A p-value tells you how likely the observed data would be if the null hypothesis were true. More precisely, it is the probability of obtaining data as extreme as what was observed, or more extreme, under the assumption that there is no real effect. This means the p-value answers: if there were no real effect, how surprising would these results be? This is why the statement that best fits is that the p-value is the probability of the observed data given the null. It does not tell you the probability that the null is true, nor does it convey the size or practical importance of an effect, nor whether results will replicate in future studies. Common misinterpretations include thinking a small p-value proves a real effect, that a large p-value proves no effect, or that significance implies real-world importance. In reality, p-values reflect the compatibility of the data with the null under repeated sampling and should be interpreted alongside effect sizes, confidence intervals, and study quality.