Statistical Significance, Power of the Test, and Effect Size Measures in Two-independent-samples t-test case
Statistical Significance, Power of the Test, and Effect Size Measures in Two-independent-samples t-test case

Summary/Abstract: Quantitative research is focused on determining the occurrence of certain population phenomena by analyzing data from a sample. Statistics is a tool that is used to check hypotheses and make decisions to reject or fail to reject such hypotheses. In this paper, the various statistical tools are reviewed: the limitations of null hypothesis significance testing and the advantages of using effect size as two measurements, which can provide important information about the results of a study. These measurements also can help interpretation of data results and easily detect trivial effects. Further, it is recommended to establish an appropriate sample size by using the optimum statistical power of the test that the research is designed. We discuss statistical significance, sample size, power of the test, and effect size, all of which have an enormous impact on how we interpret the results.

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