A result can be statistically significant but have a small effect size that is not meaningful in real-world applications. Thus, it is important to consider both STATISTICAL SIGNIFICANCE (i.e., whether the observed relationship is likely unlikely to have occurred by chance alone) and PRACTICAL SIGNIFICANCE (i.e., whether the effect size of the observed relationship is meaningful in real life).
For example: using a large probability sample and α=0.05, we find a statistically significant relationship between a new policy for processing building permits and the amount of time that it takes to process requests for building permits. We can reject the null hypothesis that there is no relationship between the new policy and processing time. Now, let’s assume the new policy is associated with a decrease in processing times of 1 day. If the average processing time before the new policy was 30 days, and the new average processing time is 29 days, the actual effect size (1 day) is minimal and may have little practical significance.