Statistical and Clinical Significance

Clinical studies reveal the effect of an intervention or the association between dependent and independent variables, which are quantifiable using metrics, such as risk ratios, mean differences, or correlations. Significance tests for null hypothesis are normally applied in determining the “statistical significance” of the effect of an intervention or treatment, which is described as a P value of < .05 (Benjamin et al., 2018). Nonetheless, researchers often misinterpret P values or give information on the effect’s importance or magnitude. Therefore, rather than just focusing on statistical significance, it is necessary to offer a reasonable estimation regarding the effect’s importance to the population from which the sample was obtained (clinical significance).

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It is possible for research to accept a null hypothesis and still meet the criterion for clinical significance. Such a study would fail to reveal a meaningful difference in effect between the treatment and control groups, but provide findings that are useful in practice and generalizable to similar research settings (Jakobsen, Wetterslev, Winkel, Lange, & Gluud, 2014). For example, in a study to establish the effectiveness of a drug to prevent nausea in pregnant mothers, the researcher may use a small sample of only 10 patients. The sample is inadequate to show meaningful differences between groups, but the drug can be concluded to be effective in preventing nausea.

Information from a qualitative study cannot meet the criteria for clinical significance if its credibility is questionable. Hence, trustworthiness, to a great extent, depends on credibility. The condition requires researchers to relate the findings with reality to reveal the trustworthiness of such findings (Polit & Beck, 2010). For example, in a study to establish the perceptions of subjects who have used medicine for the treatment of headache, the researcher may ignore the negative reviews made by the participants and report that the drug is effective. In such a case, the study would lack credibility, and hence, clinical significance.

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