What does a significant p-value typically indicate in hypothesis testing?

A significant p-value in hypothesis testing indicates that the observed data is unlikely under the assumption that the null hypothesis is true. This typically suggests that there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. When the p-value falls below a predetermined significance level (commonly 0.05 or 0.01), it signals that the results are statistically significant and that there is a meaningful effect or difference that warrants further consideration.

In the context of hypothesis testing, a significant p-value helps researchers make decisions about their hypotheses based on the evidence presented by the sample data. The lower the p-value, the stronger the evidence against the null hypothesis, leading to its rejection. This contrasts with the other choices, where a significant p-value is misinterpreted as a confirmation of the null hypothesis or indicating insufficient evidence (lack of power) to draw conclusions about the hypothesis being tested.