When statistical significance is not the same as practical significance in real life.

The goal of every researcher is to find statistically significant results in their research. If the research uses a method to test for differences between groups, you would want to find a significant difference. Similarly, when testing for the relationship between variables, you would want to find a significant relationship. However, what's crucial is not just statistical significance, but also practical significance.

Statistical tests will report results as either reaching statistical significance or not. The term "statistical significance" arises because researchers collect data from a sample, such as collecting data from 20 employees in a company, and find that the group with more work experience performs better than the group with less experience.

If a researcher finds a significant difference in this sample and wants to generalize these findings to a larger population, like all employees in all companies, they will use statistical methods to test whether the relationship between work experience and job performance is significant in the actual population or not. If it's significant, it means there's a relationship in the population. If not, it suggests that in the population, there might not be a significant relationship.

Even though the research may find a statistically significant relationship, the magnitude of that relationship can be quite small. People with more work experience might perform slightly better than those with less experience in the population, but the difference might not be substantial. This is where the concept of "effect size" comes into play.

In data analysis, researchers not only need to test for statistical significance but also calculate the effect size. If data is statistically significant but the effect size is small, it means there is a relationship, but the relationship is not practically significant. However, if a large effect size is found but it doesn't reach statistical significance, it means the relationship that is observed may not exist in the population or could be on the opposite side of the relationship.

Therefore, analysts should focus on discovering differences or relationships that are not only statistically significant but also have a practical significance that can be explained in the population group.