Statistical significance shows the mathematical probability that a relationship between two or more variables exists, while practical significance refers to relationships between variables with real-world applications, according to California State University, Long Beach. Two or more variables do not need statistical significance to have practical significance, and vice versa.
After a researcher gathers data for a study, the data typically goes into a statistical test. The results of the test also have a p-value or significance test. The most common choice for a statistical significance level is .05, which means that the probability of a relationship due to random chance is below 5 percent. The significance level helps a researcher determine whether to reject the null hypothesis, the hypothesis that states there is no relationship between the variables.
Practical significance shows that the results of the study are meaningful beyond the likelihood of chance. In order to test for application, researchers use effect size, methods of association and confidence intervals, explains Dr. Connie Schmitz of the University of Minnesota Medical School. The effect size measures the difference between the changes in the dependent variable due to the independent variable. Association varies by the type of statistical test and shows the strength in the relationship between variables. Confidence intervals determine the probability that the results are applicable to the larger population instead of just the sample.