Key result
Review outlines regression modeling principles to help clinicians interpret statistical inference in medical research.
Why the study?
Clinicians are often more concerned with clinically meaningful effects than with statistical significance, but misunderstandings about significance testing persist.
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Design
Review article explaining principles of null hypothesis significance testing
Facilitates accurate clinician interpretation of regression models; extends statistical literacy to distinguish significance from clinical relevance.
When reading a study published in a medical journal, we are usually told whether any differences in measurements or outcomes are ‘significant’. But as clinicians, we are less concerned with whether a test result is significant than whether there is a clinicallymeaningful effect; that is, an effect that is large enough to care about. In this article, I discuss the principles of null hypothesis significance testing, clarify some common misunderstandings and explain why, sometimes, the chance of a clinically meaningful effect bears little relationship with whether a test result is significant. Important statistical concepts are summarised inBox 1 and Table 1.
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David Andrew Sidebotham (2021) reported a review. This educational review explains the principles of regression modeling, including linear, logistic, and mixed-effects models, to help clinicians interpret statistical inference in medical research.
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