Background: Making appropriate decisions and drawing valid conclusions from the data requires the use of statistics in both scientific and non-scientific contexts. But errors are usually made during the formation of the result of the collected data which are obtained from a diverse and big population. Allowing errors is harmful and unavoidable, therefore, we need to control or limit the maximum level of error using statistics. Aim: Therefore, in the present review we aimed to provide brief information about the statistical test, the type of errors, and how to minimize the type of errors. Method: A unstructured literature survey was done from different online data resources such as Pubmed (NCBI), Elsevier, Springer, and Web of science. Result: In statistical interference, we expect two types of errors (Type I Error and Type II Error) which forces the results of quantitative analysis into the mold of a decision, which is whether to reject or not to reject the null hypothesis. In statistics, the statistical test will give the “p-value”. Discussion & Conclusion: In conclusion, type I error and Type II errors can be minimized by describing the level of significance and power of the study respectively.