What does a low p-value in hypothesis testing suggest?

A low p-value in hypothesis testing indicates that the observed data is unlikely to occur under the assumption that the null hypothesis is true. This suggests strong evidence against the null hypothesis, which typically posits that there is no effect or no difference in the context of the study.

When a low p-value is obtained, it implies that the results observed in the sample data would be rare if the null hypothesis were indeed true. Consequently, researchers may consider rejecting the null hypothesis in favor of the alternative hypothesis, which usually suggests that there is a significant effect or difference present.

In contrast, options that claim the null hypothesis is likely true or that the results are due to random chance do not reflect the implications of a low p-value. A low p-value actually contradicts the idea of the null hypothesis being true and undermines the notion that any observed results are due to chance alone. Additionally, asserting that there are no significant differences present in the data is inconsistent with the idea that a low p-value provides evidence against the null hypothesis, as it suggests that differences likely exist rather than confirming their absence.