Statistical significance is a measure of how likely it is that the difference in results between two groups is not due to chance alone. It is used to determine whether the results of a study are meaningful or not. The standard for deciding if a result is statistically significant is if the p-value is less than or equal to 0.05. The p-value is the probability of observing results as extreme as those in the data, assuming the results are truly due to chance alone. If the p-value is less than or equal to 0.05, it means that there is less than a 5% chance that the results are due to chance alone, and the results are considered statistically significant.

To test for statistical significance, researchers use statistical hypothesis testing, which involves stating a null hypothesis and an alternative hypothesis, selecting a probability of error level (alpha level), selecting and conducting a statistical test, and interpreting the results. When reporting statistical significance, researchers should include relevant descriptive statistics about their data, such as means and standard deviations, as well as the test statistic and p-value.

It is important to note that statistical significance does not necessarily mean practical significance. A finding may be statistically significant but may have no practical application. Additionally, relying solely on statistical significance can be problematic, as the threshold for statistical significance lacks any theoretical or practical basis. Therefore, researchers should also examine the practical significance of their findings.