What is Statistical Significance?

Statistical significance refers to the probability that a relationship formed between at least two variables is because of something specific, and not by chance or a mere coincidence. Statistical hypothesis testing helps in knowing how statistically significant outcome of a data set is. It gives a p-value that shows the probability that random chance would describe the outcome. In statistical terms, if the p-value tends to be at most 5%, it is said to be statistically significant.

When is a Result Statistically Significant?

Statistical significance enables in the acceptance or rejection of the null hypothesis. Null hypothesis showcases no relationship between a given a set of variables. A data set is said to be statistically significant when it is big enough to correctly showcase the population sample being examined. A data set having the probability of less than 1/20, or 5% is said to be statistically significant. In case, the test result is more than the p-value, it leads to the acceptance of the null hypothesis. On the other side, if the p-value is more than the test result, it results in the rejection of the null hypothesis.