Which statement is a common misinterpretation of p-values?

• A. A p-value is the probability that the null hypothesis is true.
• B. A p-value is the probability of observing the data given the null is true.
• C. A p-value is the probability of Type I error.
• D. A p-value is the probability that the study will be replicated.

Answer: (B)

The main idea being tested is how p-values should be interpreted. A p-value represents the probability of obtaining data as extreme as what was observed (or more extreme), assuming that the null hypothesis is true. This conditional perspective is what makes the statement that a p-value is the probability of observing the data given the null is true the best choice. It clarifies that we’re evaluating the data under the assumption of no effect, not making a direct claim about the truth of the null itself.

This is important because p-values do not tell us the probability that the null hypothesis is true. They also are not the probability of making a Type I error in a single study, nor a prediction about whether the study will be replicated. Those are distinct concepts. The misinterpretations—thinking the p-value gives the likelihood the null is true, or equating it with Type I error probability, or using it to forecast replication—result from conflating conditional probabilities or overextending what the p-value conveys.