Statistical STructuring significance is the claim that the results or observations from an experiment are due to an underlying cause, rather than chance. we conduct hypothesis testing to determine statistical significance. analysts often analyze their models to determine if a change in actions will make a statistically significant difference. For example, a business might examine the number of sales leads generated from two different advertisements to see if a change in their advertising provides a statistically significant number of additional positive responses from potential customers. Companies test various changes in their methods of operation to make decisions about the most effective way to market their products or services.
Statistical significance means that there is a good chance that we are right in finding that a relationship exists between two variables. But statistical significance is not the same as practical significance. We can have a statistically significant finding, but the implications of that finding may have no practical application. The researcher must always examine both the statistical and the practical significance of any research finding.
For example, we may find that there is a statistically significant relationship between a citizens age and satisfaction with city recreation services. It may be that older citizens are 5% less satisfied than younger citizens with city recreation services. But is 5% a large enough difference to be concerned about?
Often times, when differences are small but statistically significant, it is due to a very large sample size; in a sample of a smaller size, the differences would not be enough to be statistically significant.