Statistical Versus Clinical Significance
Statistical Versus Clinical Significance
Implementation into Practice with Rationale, Risk Analysis, and Cost
Statistical significance occurs when a finding allows an investigator to discard a null hypothesis at a pre-specified level of confidence or probability. The level is referred to as alpha, and it is typically set at 0.05 that implies that the chances of false positive results are at 5 percent. In critical research, alpha is usually set at 0.01 so as to make the likelihood of false positive results to be at 1 percent. Therefore, this helps in confirming that the critical results are real rather than random. On the other hand, clinical significance is a change in a subject’s clinical status through the reflection of the degree or size of the variance that is regarded as important. Many researchers have supported the application of clinical significance as equated to statistical importance when examining clinical problems. Clinical significance probes the bigger query of differences since the knowledge that two entities are dissimilar is not enough for one to deduce a conclusion. Therefore, this implies that the researchers need to know the magnitude of the difference and the inaccuracy allied with the appraisal.
The article indicated that a statistically significant difference is an association that occurs among the variables that were not caused solely by normal variation. The researcher quoted the study carried out by Swartz-ell et al.
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(2013) that was based on the relationship that exists between fall risk score and actual occurrence of falls in acute care patients diagnosed with diabetes, stroke, and heart failure (Heavey, 2015). In the research, diabetes patients depicted a statistically significant variance among the scores for the patients who fell and those who did not fall (p = 0.02 alpha = 0.05). Statistically, the p-value is used to indicate that the results are just a random occurrence and not an exact difference between the variable. Therefore, if the p-value under analysis is less than the alpha level set by a researcher, then the results are statistically important and improbable to be a random occurrence.
Determining Statistical Significance
In the study carried out by Swartz-ell, the analysis results in a probability value that was less than the alpha value that was intended for the research. Thus, this implies that they were confident that the difference they found was real and had not occurred by chance. Similarly, it showed that the fall risk score had a correlation with the actual risk of falling in a diabetic patient. The patients who have a high fall risk were likely to fall on many occasions as compared to those with a low fall risk score (Heavey, 2015). Therefore, this implies that the investigators had determined a statistically significant variation.
Determining Clinical Significance
An extra step is required for a researcher to establish on whether a statistically significant difference depicts the characteristics of clinical significance. A difference is deemed to be clinically significant when professionals in the nursing field believe that a statistical difference is substantial enough to be clinically relevant and it should direct the progress of patient care. The research by Swartz-ell et al. established a statistical difference only in diabetic patients and not among patients with heart failure or stroke (p= 0.729, alpha = 0.05). Despite the difference, the instrument failed to identify a fall risk for 44 percent of the patients who dropped. Thus, due to the limited ability to accurately detect a fall risk across multiple patient populations, the results produced by statistical significance may not be useful due to their lack of clinical significance. Therefore, if a result that is statistically significant is not clinically significant, it should not be used to guide clinical practice since the results are not clinically helpful.
Researchers carry out risk analysis on the acceptable risk of falsely rejecting a null hypothesis and that of an acceptable risk of falsely accepting a null hypothesis. Similarly, the process is faced with both direct and indirect costs due to the rigorous clinical trials that are done under clinical significance so as to approve data accepted under statistical significance. Despite the drawbacks associated with this rationale, I would implement the method into my practice due to the accuracy related to the results.
Heavey, E. (2015). Differentiating statistical significance and clinical significance. American Nurse Today, 10(5), 26-28.
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