What is Statistical Significance?

Research uses the term statistical significance to ascertain whether the observed difference or effect between groups is not mere chance but true. The researchers, in this way, can understand whether the study results are meaningful, or whether they could have happened just by chance.

Usually, researchers fix a level of significance (often called; α ) which denotes their cut-off point where results are considered statistically significant. The most frequently used level of significance is 0.05 implying that if the possibility of having obtained similar findings under the null hypothesis [i.e., no effect or difference] is less than 5%, then it can be said to be statistically significant.

To determine statistical significance, researchers generally engage in hypothesis testing such as t-tests, chi-square tests, ANOVA and regression analysis depending on the nature of the data and research question involved. These tests yield a p-value indicating the probability of getting these outcomes assuming the null hypothesis holds. The results are considered statistically significant when the p-value is less than those n level of significance and the null hypothesis is rejected for an alternative one instead.

Level of significance or Statistical significance is an important terminology used in Statistics. Level of significance is the measurement of the statistical significance. The level of significance explains whether the null hypothesis is accepted or rejected. In this article, we are going to discuss the level of significance in detail.