What is the significance of a p-value less than 0.05 in hypothesis testing?

A p-value less than 0.05 indicates that the results of a statistical test are considered statistically significant. In hypothesis testing, this means that there is strong evidence against the null hypothesis, suggesting that the observed data is unlikely to have occurred under the null hypothesis’ assumption.

When a p-value is below this threshold, it typically leads to the rejection of the null hypothesis, implying that there is a statistically significant effect or difference present in the data. This is a common criterion used in many scientific disciplines to decide whether findings should be regarded as noteworthy or reliable.

While it's true that a p-value of less than 0.05 also means that there is a less than 5% probability of committing a Type I error (which refers to the incorrect rejection of a true null hypothesis), this interpretation does not directly define significance itself. Therefore, while this information supports the concept of why a p-value less than 0.05 is important, the core reason is that it demonstrates statistically significant results, which is why that answer is the primary focus here.