**Core Concept:** A P-value is a statistical measure used to determine the likelihood of observing a specific result due to chance alone in a hypothesis test. It ranges from 0 to 1, with lower values indicating stronger evidence against the null hypothesis. A P-value less than 0.05 is generally considered statistically significant, suggesting that the observed results are unlikely to be due to random chance.

**Why the Correct Answer is Right:** The correct answer, D, highlights an important aspect of P-values: they do not indicate the probability of a specific effect occurring. Instead, they assess the probability of obtaining the observed data or more extreme data given the null hypothesis is true. Therefore, P-values cannot be used to determine the likelihood of a treatment's effect size or its practical significance.

**Why Each Wrong Option is Incorrect:**

A. **False:** P-values do not represent the probability of the effect occurring; they only assess the probability of obtaining the observed data or more extreme data given the null hypothesis is true.

B. **False:** P-values do not directly indicate the strength or size of an effect; they only provide information about the probability of observing the data or more extreme data under the null hypothesis.

C. **False:** P-values do not account for the alternative hypothesis; they only deal with the probability of observing the data or more extreme data under the null hypothesis.

**Clinical Pearl / High-Yield Fact:** Remember that P-values are crucial in hypothesis testing but do not determine the practical significance of an effect or its magnitude. Interpret P-values in conjunction with effect sizes, confidence intervals, and clinical context to make informed decisions about the practical importance of observed findings.