What does statistical significance indicate?

Statistical significance primarily indicates the likelihood that an observed effect in a study is not a result of chance. When results are deemed statistically significant, they suggest that the findings are strong enough to be considered reliable and are unlikely to have occurred randomly. This is often quantified using a p-value, which helps researchers determine whether the evidence against the null hypothesis (typically representing no effect) is sufficient to reject it.

In practice, findings that are statistically significant provide a basis for concluding that there is an underlying effect or relationship in the data being studied, implying that the treatment or intervention has made a discernible difference. This concept is essential in helping researchers and decision-makers understand the reliability of their results and whether they should act upon them.

The other options do not accurately capture the essence of statistical significance. For example, saying the results are "definitely caused by a treatment effect" overlooks the possibility of alternative explanations or the various factors that could influence the findings. Similarly, referencing the probability of making a type I error specifically addresses the risk associated with claiming a result is significant when it is not, but does not convey the broader meaning of significance itself.