*HCA 699 What is the difference between statistically significant evidence and clinically significant evidence?*

HCA 699 What is the difference between statistically significant evidence and clinically significant evidence?

“Clinical significance of a result is dependent on its implications on existing practice-treatment effect size being one of the most important factors that drives treatment decisions. LeFort suggests that the clinical significance should reflect “the extent of change, whether the change makes a real difference to subject lives, how long the effects last, consumer acceptability, cost-effectiveness, and ease of implementation”. Statistical significance is heavily dependent on the study’s sample size; with large sample sizes, even small treatment effects (which are clinically inconsequential) can appear statistically significant; therefore, the reader has to interpret carefully whether this “significance” is clinically meaningful” (Ranganathan, Pramesh, & Buyse, 2015, para. 2).

The difference between statistically significant evidence and clinically significant evidence is most clinical research that are statistically significant are often interpreted as being clinically important. On the other hand, statistical significance indicates the reliability of the study results typically reflects an impact as clinical practice. Statistically significant evidence is often times referred to as an absolute value of S is greater than the critical value this would ultimately give you the formula of the to the answer of the equation. Clinically significant evidence is more related to the abstract not so much based on the trend that usually most evidence would come from. The way each of these findings would be used to advance an evidenced based project would be very interesting. I think the more concrete method to use would be statistically significant evidence because it tends to lead more to the certain and concrete evidence more than a guess or some one’s opinion. When using clinically significant evidence it is more based on some one’s own research and the determination or conclusion they were able to come to using the formula or information they had at the time. These two different types of methods can be confusing at times because the user may not know what avenue to take but depending on the evidence you are currently looking for that will kind of steer you in the direction. Listening to what you are looking for is extremely important because picking the wrong evidence will have the information you receive be very incorrect.

### References

Ranganathan, P., Pramesh, C. D., & Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. *Perspectives in Clinical Research, 6*(3), 169-170. doi: https://doi-org.lopes.idm.oclc.org/10.4103/2229-3485.159943

### RESPOND

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It appears that people often misinterpret these two types of evidence as being one and the same. Mainly because there is a confusion that stems from the word significant as meaning it is literally important (Ranganathan, Pramesh, Buyse, 2015). For statistics specifically, it has a completely different implication than with clinically applicable findings (Ranganathan et al., 2015). So statistically significant is about “quantifying the probability of a study’s results being due to chance. Clinical significance, on the other hand, refers to the magnitude of the actual treatment effect” (Ranganathan et al., 2015, p.169).

When it comes to advancing an evidenced-based project it appears that clinical significance is more of what is being sought. Because it is about the findings that are related to the control groups and receiving the intervention being researched (Ranganathan et al., 2015). If the results are strong enough within their trial than it can greatly impact best practice (Ranganathan et al., 2015). If an intervention produces the results in favor of the hypothesis then it can be applied to real-life medical care. This strongly helps advance any evidenced-based project because of its established applications to care.

Now statistical significance is still important however it is far more dependent on several variables one of which is

the sample size of the study (Ranganathan et al., 2015). The larger the sample size the more the findings can appear to be statistically significant even if the treatment effects are inconsequential (Ranganathan et al., 2015). There is more responsibility put on the readers evaluating the study to determine if the statistically significant findings are clinically applicable (Ranganathan et al., 2015). One such example of this was a study done on patients with advanced pancreatic cancer who received treatment with erlotinib plus gemcitabine versus a group who only received gemcitabine alone in their treatment (Ranganathan et al., 2015). The survival rates were found to be prolonged in the groups that received both treatments with a p-value being about 0.038 (Ranganathan et al., 2015). This translates to there being “only a 3.8% chance that this observed difference between the groups occurred by chance…and therefore, [is] statistically significant” (Ranganathan et al., 2015, p.170). Despite being statistically significant it shows the treatment effect did not produce enough of the median survival rate and is therefore clinically irrelevant when it comes to measuring the outcomes of this suggested combination (Ranganathan et al., 2015).

Overall for statistical significance, context is everything and is not as easily applied for EBP as the findings that are considered clinically significant. The best way to differentiate between the two is through the way that Melnyk & Fineout-Overholt explain it which is that clinical significance is how the impact the findings will have on clinical practice, and statistical significance is that the results did not occur by happenstance (2019). Thus, as a reader of these studies, it is important to be cognizant of these two types of findings and be able to infer them correctly when considering an application to practice. Each does offer something to advance an EBP but may not be applicable in every situation. This has to be decided once the variables are considered and ruled out.

Ranganathan, P., Pramesh, C., & Buyse, M. (2015). Common pitfalls in statistical analysis: Clinical versus statistical significance. *Perspectives in Clinical Research, 6(3),* 169-170. Retrieved from http://www.picronline.org/article.asp?issn=2229-3485;year=2015;volume=6;issue=3;spage=169;epage=170;aulast=Ranganathan

Melnyk, B., & Fineout-Overholt, E. (2019). *Evidence-based practice in nursing & healthcare a guide to best practice 4th edition* [Ebook Version]. Retrieved from https://www.gcumedia.com/digital-resources/wolters-kluwer/2018/evidence-based-practice-in-nursing-and-healthcare_a-guide-to-best-practice_4e.php