What does statistical significance in research indicate?

Statistical significance in research primarily indicates that the observed results are unlikely to be due to random chance. This means that there is a low probability (typically less than 5%, or a p-value of less than 0.05) that the results observed in a study occurred solely by random sampling error. When researchers find statistically significant results, it suggests that there is likely a true effect or relationship present in the population from which the sample was drawn.

This concept is essential in hypothesis testing, where researchers aim to determine whether their findings can be generalized beyond the specific sample studied. Establishing statistical significance helps to bolster the evidence for theories or interventions being tested, suggesting they are not merely artifacts of random variability.

Other options exist that could mislead conclusions. For instance, statistical significance does not guarantee that the results are always accurate and reliable, nor does it imply that there is a perfect relationship between variables. Additionally, while a large sample size can increase the robustness of a study's findings, statistical significance does not exclusively depend on the sufficiency of sample size; even small samples can yield significant results if the effect is strong enough or if the variability is low. Thus, the correct interpretation of statistical significance focuses on the improbability of chance as a sole explanation