| Popis: |
Ruling out that there is absolutely no effect or association between variables may be a good first step, but it is rarely the ultimate goal of science. Yet that is the only inference provided by traditional null hypothesis significance testing (NHST), which has been a mainstay of many scientific fields. Reliance on NHST also makes it difficult to define what it means to replicate a finding, and leads to an uncomfortable quandary in which increasing precision in research reduces researchers’ ability to perform theory falsification. To solve this problem, in recent years several alternatives to traditional NHST have been proposed. Yet few researchers are familiar with these alternatives because each new test is described using its own terminology and practiced in different fields. We describe a simple framework for conceptualizing all these tests so that it is not necessary to learn them separately. Moreover, the framework allows researchers to conduct any of these tests by asking just one question: is the confidence interval entirely outside the null region(s)? This framework will help researchers choose the test(s) that best answers their research question when simply ruling out “no effect at all” is not enough. |
