What should be concluded if a p-value is greater than the significance level?

When a p-value is greater than the predetermined significance level, it indicates that there is not enough statistical evidence to reject the null hypothesis. In hypothesis testing, the null hypothesis typically represents a default position or a statement of no effect or no difference.

A high p-value suggests that the observed data is consistent with the null hypothesis and that any observed differences might be due to random chance rather than a true effect. Therefore, in this context, researchers typically say that one fails to reject the null hypothesis, which can be interpreted as "accepting" the null hypothesis, although technically, it is more correct to say that there is insufficient evidence to reject it.

The conclusion does not imply that the null hypothesis is true or proven; rather, it emphasizes that based on the data collected and the chosen significance level, one does not have sufficient evidence to support the alternative hypothesis. This is why this choice correctly reflects the interpretation of hypothesis testing in situations where the p-value exceeds the significance level.

The other options either suggest an active acceptance of the null (which is not technically accurate), imply a lack of clarity or decision in the statistical test, claim that the alternative hypothesis is definitively proven (which goes against the principles of hypothesis testing), or propose adjustments to