What is a p-value?

What is a p-value?

• A. The probability that the null hypothesis is true.
• B. The probability that the study results are clinically significant.
• C. The probability that the alternative hypothesis is true.
• D. The probability of observing data as extreme as or more extreme than observed under the null hypothesis.

Answer: (D)

A p-value is the probability, under the assumption that the null hypothesis is true, of observing data as extreme as or more extreme than what was actually observed. In other words, it asks how surprising our data would be if there were really no effect. A small p-value suggests the observed result is unlikely under the null and provides evidence against it at a chosen significance level; a large p-value means the data are compatible with the null. Remember what it is not: it is not the probability that the null hypothesis is true, nor a measure of clinical significance or the probability that the alternative hypothesis is true. The p-value reflects the data given a specific model and the null assumption, and it depends on the sample size and the test used. Large samples can produce small p-values for trivial effects, while small samples can yield large p-values even when there is a meaningful effect. Interpret the p-value alongside the effect size and confidence intervals to get a fuller picture.

A p-value is the probability, under the assumption that the null hypothesis is true, of observing data as extreme as or more extreme than what was actually observed. In other words, it asks how surprising our data would be if there were really no effect. A small p-value suggests the observed result is unlikely under the null and provides evidence against it at a chosen significance level; a large p-value means the data are compatible with the null.

Remember what it is not: it is not the probability that the null hypothesis is true, nor a measure of clinical significance or the probability that the alternative hypothesis is true. The p-value reflects the data given a specific model and the null assumption, and it depends on the sample size and the test used. Large samples can produce small p-values for trivial effects, while small samples can yield large p-values even when there is a meaningful effect. Interpret the p-value alongside the effect size and confidence intervals to get a fuller picture.