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Evidence-based practice can be implemented by the nurses only after the comprehension and interpretation of the research. Thus, comprehension and a clear distinction between statistical and clinical significance are mandatory. Though these terms are not the same thing, they are highly related. Statistically significant results quantify the probability of the findings of the study being due to chance. Clinical significance is the practical importance referring to the magnitude of the treatment effect (difference between control and intervention groups, known as “treatment effect size”) that determines whether the trial results might impact current medical practice. Statistical significance is represented by the “P” value indicating the probability that study findings are due by chance rather than to a real treatment effect. The conventional cut-off value for “P0” is considered statistically significant at 0.05 or 5%. When P<0.05, it indicates the possibility of the results of a study by chance being <5%. However, clinically significant evidence is based on its implications on the prevailing practice-treatment effect size is an important factor driving treatment decisions. It reflects the extent of change, the real difference made due to the change, how long the effects last, cost-efficacy, consumer acceptability, and effectiveness of implementation. Traditionally accepted values are available for statistical significance testing; however, it is lacking in the evaluation of clinical significance. Statistical significance is greatly dependent on the sample size of the study; hence, even larger sample sizes or smaller treatment effects can bring statistically significant evidence (Schober et al., 2018).
Evidence-based practice (EBP) influences clinical decision-making, but at times it becomes difficult for clinicians to make decisions purely on the basis of statistical data. Statistically, significant evidence simply addresses whether to reject or accept the directional or null hypothesis, without much information on the direction or magnitude of the difference (treatment effect). Statistically, significant evidence may not always provide an appropriate change in clinical practice. Thus, clinical interpretation of research or clinically significant evidence for treatment outcomes is important because it can facilitate clinical decision-making that includes efficacy and patient safety. And, from a clinical perspective, statistically, significant differences bring limited influence. Whereas, confidence intervals and treatment effect calculations provide more valuable information for clinicians to appraise the evidence and apply them in clinical practice (Glen, 2021).
References:- Glen, S. (2021). Clinical significance. Retrieved from https://www.statisticshowto.com/clinical-significance/
Schober, P., Bossers, S., &Schwarte, L. A. (2018). Statistical significance versus clinical importance of observed effect sizes: What do p values and confidence intervals really represent? Anesthesia& Analgesia, 126(3), 1068-1072. https://journals.lww.com/anesthesia-analgesia/Fulltext/2018/03000/Statistical_Significance_Versus_Clinical.48.aspx
