Alternatives to null hypothesis significance testing

Despite years of criticism, null hypothesis significance testing (NHST) continues to be psychology's most widely employed model of statistical inference. Since Fisher (1925), psychologists have routinely used the model to dismiss sampling error when making substantive inferences, despite the deep methodological and philosophical flaws inherent in the hypothesis-testing procedure. Part of this problem may be due to a perceived lack of available alternatives to traditional null hypothesis significance testing. The present research sought to search for and evaluate existing alternatives to current NHST. The central question asked was whether there exist any feasible and noteworthy alternatives to today's NHST model. This article provides a summary and review of alternative theory-testing models which are evaluated based on their ability to either complement or altogether replace today's NHST. It seeks to spread awareness that choices do exist for the psychologist and social scientist alike when "shopping" for a theory-testing paradigm. It is concluded that through the use of effect sizes, confidence intervals, graphical methods, "good-enough" hypotheses, and in comparing alternative models to account for sample data, there exist a number of useful and very practical alternatives to traditional NHST. Several of these approaches are recommended for use in psychology and abroad. Power analysis is also reviewed, and although deemed a useful complement to NHST, the addition of power-analytic strategies does not "save" the problematic paradigm.