A statistical test estimates how consistent an observed statistic is compared to a hypothetical population of similarly obtained statistics – known as the test, or ‘null’ distribution. The further the observed statistic diverges from that test population’s median the less compatible it is with that population, and the less probable it is that such a divergent statistic would be obtained by simple chance. That compatibility is quantified as a P-value – where a low P-value indicates your observed statistic is an extreme quantile of the distribution it being tested against. Perhaps, in some samples where significance level is somewhere around or between 0.1 percent, and where samples contain small cases, then more relaxed levels of significance maybe be employed. However, as a scholar-practitioner, it is ideal to understand the essence of significance level and determine whether it can be relaxed or not. Consequently, statistical level of significance has been misconstrued by many researchers (Sham & Purcell, 2014). For example, whenever we have population random sampling or the sample didn’t signify the all-inclusive populace, we then have to undertake statistical significance analysis. One of the things researchers can do is to reject sampling error by gathering data from the entire population. The verdict to reject the null hypothesis is the concept of statistical significance (Carver, 1978).
Carver, R.P. (1978). The case against statistical significance testing. Harvard Educational Review, 48, 378-399.
Sham, P. C., & Purcell, S. M. (2014). Statistical power and significance testing in large-scale genetic studies. Nature Reviews Genetics, 15(5), 335-46
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