Hello,

I started the discussion thread, but I am struggling to find 2 sources that support my content. Sources must have been published within the last five years and should include peer-reviewed journal articles, published textbooks.

Please help!

The Case Against Statistical Significance Testing, Revisited

The author was not biased because there is a concerning trend of labeling results as significant when they are insignificant in today's research landscape. Sometimes, authors mistakenly use the term "significant" when they mean "important," leading to confusion among readers or the intended audience. Adding "statistically" in front of "significance" is crucial, as it can help dispel misconceptions among readers.

I agree with Carver's suggestion that journal editors' selection should take into account their stance on statistical significance testing. Editors who view statistical significance as a guarantee of research worthiness and those who question or discredit the validity of statistical significance testing must be avoided. Good scientific practice hinges on ethical research conduct and the objective, impartial interpretation of data rather than the mere use of statistical significance testing by researchers.

As theBible states inTimothy 2:15, "Do your best to present yourself to God as one approved, a worker who does not need to be ashamed and who correctly handles the word of truth."

I'm afraid I have to disagree with Carver's assertion that "statistical significance testing is somehow a corruption of the scientific method." His argument implies that authors should thoroughly examine their data outcomes and research questions before conducting statistical tests. Nevertheless, I strongly object to this viewpoint because, when appropriately performed, statistical significance testing is an essential pillar of the scientific method. A well-conducted hypothesis testpreciselyaligns the data with the hypothesis, as the research question determines the theory tobe testedand the appropriate statistical technique for analysis. Furthermore, it's imperative to validate the assumptions as part of the statistical analysis rigorously.