Clinically significant

In medicine and psychology, clinical significance is the practical importance of a treatment effect - whether it has a real genuine, palpable, noticeable effect on daily life.

Statistical significance is used in hypothesis testing, whereby the null hypothesis (that there is no relationship between variables) is tested. A level of significance is selected (most commonly alpha = 0.05 or 0.01), which signifies the probability of incorrectly rejecting a true null hypothesis. If there is a significant difference between two groups at alpha = 0.05, it means that there is only a 5% probability of obtaining the observed results under the assumption that the difference is entirely due to chance (i.e., the null hypothesis is true); it gives no indication of the magnitude or clinical importance of the difference. When statistically significant results are achieved, they favor rejection of the null hypothesis, but they do not prove that the null hypothesis is false. Likewise, non-significant results do not prove that the null hypothesis is true; they also give no evidence of the truth or falsity of the hypothesis the researcher has generated. Statistical significance relates only to the compatibility between observed data and what would be expected under the assumption that the null hypothesis is true.

In broad usage, the "practical clinical significance" answers the question, how effective is the intervention or treatment, or how much change does the treatment causes. In terms of testing clinical treatments, practical significance optimally yields quantified information about the importance of a finding, using metrics such as effect size, number needed to treat (NNT), and preventive fraction. Practical significance may also convey semi-quantitative, comparative, or feasibility assessments of utility.

Effect size is one type of practical significance. It quantifies the extent to which a sample diverges from expectations. Effect size can provide important information about the results of a study, and are recommended for inclusion in addition to statistical significance. Effect sizes have their own sources of bias, are subject to change based on population variability of the dependent variable, and tend to focus on group effects, not individual changes.

Although clinical significance and practical significance are often used synonymously, a more technical restrictive usage denotes this as erroneous. This technical use within psychology and psychotherapy not only results from a carefully drawn precision and particularity of language, but it enables a shift in perspective from group effects to the specifics of change(s) within an individual.

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