In terms of statistical significance, a p-value less than 0.05 typically indicates what?

A p-value less than 0.05 is commonly interpreted as strong evidence against the null hypothesis in the context of statistical hypothesis testing. When researchers conduct experiments or studies, they often have a null hypothesis that represents a default position or a statement of no effect or no difference. The p-value quantifies the probability of observing the collected data, or something more extreme, given that the null hypothesis is true.

When the p-value falls below the threshold of 0.05, it suggests that the observed data would be quite unlikely if the null hypothesis were correct. This low probability leads researchers to reject the null hypothesis in favor of the alternative hypothesis, which posits that there is an effect or a difference. The conventional 0.05 level is a cutoff that balances the risk of making a Type I error (rejecting a true null hypothesis) with practical significance in research, making it a widely accepted standard for identifying statistically significant results.

In summary, a p-value under 0.05 indicates statistically significant results, providing strong evidence that the null hypothesis may not be true.