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Discussion: Statistical and clinical significance.

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There exists some confusion surrounding the differences between statistical and clinical significance. Readers of medical research view statistical significance as translating to the data holding a clinically important outcome (Ranganathan, Pramesh, & Buyse, 2015). This is not the case. Instead statistical significance informs if there is a difference or a relationship between the independent and dependent variables. Statistical significance does not mean that the difference or relationship equates to clinical significance (Chamberlain, 2019). With statistical significance the numbers conclude that the outcome is not due to chance. Clinical significance refers to the practicality of the findings. In real life situations, would these results have an impact on clinical practice? Some research may have statistical significance but may not be clinically significant, thus not resulting in a clinical change in healthcare practice (Heavey, 2015).

It is possible to have research study results that supported the acceptance of the null hypothesis and demonstrate clinical significance. For example, if a study hypothesized that a new diabetes medication had no effect on blood sugar levels and the null hypothesis could not be rejected, it is plausible that the data is not statistically significant but it can still be clinically significant. If even a small number of subjects in the study showed a decrease in blood sugar levels attributed to the introduction of the medication, this could have an effect on the clinical practice in treating such patients going forward.

Credibility has to do with verifying the results of the study as accurate or believable. In terms of qualitative research this will rely on the foundation of information gathered during either the observations or participant interviews. Credible data is more likely to result from a study that has appropriate and adequate data, as well as a good analysis of negative cases. If a study’s credibility is in question, clinical significance in practice would not be established. Credibility may be established in a variety of ways, and when these techniques are not employed it is likely the study will not be credible. One such method is triangulation. Triangulation involves using multiple methods, data sources, observers, or theories surrounding the phenomenon being studied so as to ensure a more complete representation of reality. Triangulation ensures credibility by supporting comprehensive research findings. If methods such as these are not employed, it is unlikely the findings will have clinical significance. For example, if a study to determine if physical activity affected senior mental  health did not employ methods such as triangulation and therefore was deemed not to be credible, there would be no way in which to know if there is a correlation between the two in order to be significant to clinical practice with seniors.   (Polit & Beck, 2017).


Chamberlain University. (2019) Retrieved from

Heavey, E. (2015). Differentiating statistical significance and clinical significance. American Nurse Today, 10(5), 26-28

Polit, D. & Beck, C. (2017). Essentials of nursing research: Appraising evidence for nursing practice (9th ed.). Philadelphia, PA: Wolters Kluwer.

Ranganathan, P., Pramesh, C.S., Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspectives in Clinical Research, 6(3), 169-170.

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