What is the implication of a p-value lower than the significance level of a test?

A p-value represents the probability of observing the test results, or something more extreme, under the assumption that the null hypothesis is true. When the p-value is lower than the predetermined significance level (commonly set at 0.05 or 0.01), it indicates that the observed data is sufficiently unlikely under the null hypothesis. This low p-value provides strong evidence against the null hypothesis, suggesting that it does not adequately explain the observed data.

Consequently, the correct action in this scenario is to reject the null hypothesis. This does not confirm a specific alternative hypothesis but rather indicates that the data provides enough evidence to doubt the validity of the null hypothesis.

In contrast, accepting the null hypothesis is not warranted when the p-value is low, as it would contradict the statistical evidence. Additionally, a low p-value does not necessitate more data collection; rather, it shows that the collected data is already significant enough to warrant rejecting the null hypothesis. Lastly, a significant p-value indicates that the results are conclusive enough not to be termed as inconclusive.