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

A p-value less than 0.05 indicates that the observed data is statistically significant, suggesting that the null hypothesis can be rejected. In hypothesis testing, the null hypothesis typically represents a statement of no effect or no difference. A p-value lower than 0.05 suggests that the likelihood of observing the data, or something more extreme, under the assumption that the null hypothesis is true is less than 5%.

This low probability suggests that there is strong evidence against the null hypothesis and supports the idea that the alternative hypothesis, which proposes a significant effect or difference, could be true. While it does not provide absolute certainty and does not confirm the alternative hypothesis, it provides a strong reason to consider it as a valid explanation for the observed results. The threshold of 0.05 is a conventional cutoff in many scientific fields, indicating a common standard for determining statistical significance.

In summary, a p-value below 0.05 strengthens the case for rejecting the null hypothesis, reinforcing the idea that an effect or difference exists.