NUR 699 Week 6 Discussion 2

 NUR 699 Week 6 Discussion 2


Statistical significance is the probability that the observed outcome in a study would have occurred by chance if the treatment really had no effect. Statistically significant findings indicate that an observed effect is unlikely to be due to chance but instead are likely due to the independent variable (the cause). In contrast, clinically significant evidence is necessary for something to be labeled as proven treatment for an illness.

When designing trials to evaluate new treatments, researchers must choose between two approaches: the statistically significant method and the clinically significant method. Each method has its strengths, but clinicians must be careful about committing a false positive error. For example, if a new treatment is said to work when it really doesn’t, this would lead to the use of an ineffective treatment; on the other hand, if the null hypothesis is true and there is no real difference between treatments, this would lead to the use of an effective treatment that might not actually be effective in all cases. The gold standard of clinical trials is certainly greater than are false positives as well as lower than false negatives.

For researchers to be able to say that there is a statistically significant difference between two groups, the finding must be replicated or there must be multiple studies that support the finding. Researchers then look at the clinical significance of the finding. If a change was observed between groups in a laboratory setting, researchers determine if this change has an impact on what health professionals would consider important. For example, let’s examine the following three bladder cancer studies:

Statistical significance includes data analysis, sampling techniques, and hypothesis testing. Clinical significance involves those diagnoses with a high association of risk for an individual or group. If a project is aimed at the use of treatments for heart disease, the findings would be deemed statistically significant because there is only one outcome (death from a heart attack or death from any cause). With cancer, however, the results are considered clinically significant because there are various outcomes (death from a heart attack or death from any cause).

The concept of clinically significant therapy effect was introduced to contrast the observed magnitude of an experimental treatment effect with the minimal size that would be needed to have a meaningful impact on clinical practice [1]. The dashed line in (Panel A) is conceptually equivalent to a commonly used threshold for clinical significance in medical research [2], which suggests that effects are clinically relevant if they are of “practical importance” and can be “demonstrated unequivocally … beyond all reasonable doubt, by a high degree of statistical significance.”


What is the difference between statistically significant evidence and clinically significant evidence? How would each of these findings be used to advance an evidenced-based project?

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