Clinical significant and statistical significant

Comment1

Clinical significant and statistical significant are both used to interpret research studies.

Clinical significant “is a subjective interpretation of a research result as practical or meaningful for the patient and thus likely to affect provider behavior”. (Thompson, 2017). It is the practical importance of a research study in relation to effect to patient outcome and whether implementation of study result will promote positive patient outcome. Clinical significance indicates how meaningful study result is. “For some conditions, very small changes could have large impact on symptomatology, disease, or quality of life”. It is a subjective decision that is based on what condition is being studied or number of people affected. (Thompson, 2017).

Statistical significant “has to do with the likelihood that a research result is true”. It is the real effect of the intervention. Statistical significant results are used in making objective decisions. Its effectiveness depends on sample size. With large sample size, there would be statistical significance in almost everything, and if the sample size is not large enough chances of random error is increased and results may not show significant differences. (Thompson, 2017). The power of statistics helps in making a correct decision especially, in rejecting the null hypothesis when it is actually false. “Statistical significance tells us how likely a research result is a chance finding based on the researchers predetermined significance level”. Findings may not be important enough to affect clinical practice change even when found to be statistically significant. (Thompson, 2017).

comment2

Clinical significance involves a decision that is made based on the practical experience value or relevance of a particular treatment. Clinical significance does not necessarily involve statistical significance as an initial criterion (Friedman, 2014).

On the other hand, statistical significance involves an outcome of research that can be indicated to be true. This differs from clinical significance that can be said to be a subjective interpretation of the results from research that only offers practical experience to a patient (Peake, 2013). Further, statistical significance involves large sample size in research analysis while clinical significance only requires minimal size. While statistical significance research requires more time, effort and high costs, clinical significance does not need much time or money to be carried out (Friedman, 2014).

Additionally, clinical significance is used to describe the effects of the treatment of disease while statistical significance is not. Statistical significance further occurs along a continuum while clinical significance looks at quantitative differences between groups sample of the population is due to chance. If statistical significance is not carried out correctly such as using a large amount of sample, random errors will increase and resulting in incorrect results. However, clinical significance does not depend on correct data as the results can be reported using risk measures (Peake, 2013).

 
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