Effect Size and Practical and Statistical Significance

Effect Size and Practical and Statistical Significance Order Description A brief summary of the article you selected. Then briefly explain" rel="nofollow">in the in" rel="nofollow">interrelationship between the effect size, power, and sample size in" rel="nofollow">in the article. Explain" rel="nofollow">in whether the effect size was practically significant, statistically significant, or both, and why. Usin" rel="nofollow">ing the article you selected as an example, support the importance of in" rel="nofollow">incorporatin" rel="nofollow">ing both practical and statistical significance in" rel="nofollow">in study conclusions. Effect size refers to the strength of a relationship between two variables. It can be expressed through a difference or ratio. Odds ratios, which compare the likelihood of the same event occurrin" rel="nofollow">ing within" rel="nofollow">in two separate groups, reflect effect size. Consider the followin" rel="nofollow">ing two scenarios: Ten thousand people participated in" rel="nofollow">in a survey in" rel="nofollow">in which they were asked whether they use a vitamin" rel="nofollow">in C supplement and also whether they had experienced cold symptoms the previous win" rel="nofollow">inter. The study demonstrated an odds ratio of 1.08 and a p-value of 0.04. Based on this, the researchers recommended the use of vitamin" rel="nofollow">in C supplements to avoid experiencin" rel="nofollow">ing a cold next win" rel="nofollow">inter. Fifty people participated in" rel="nofollow">in a survey about stress and a diagnosis of high blood pressure. This study resulted in" rel="nofollow">in an odds ratio of 1.6 and a p-value of 0.07. Based on these results, the study was not published and the researchers moved on to other work. These two hypothetical situations are extremes, but they illustrate the difference between practical and statistical significance. Practical significance is in" rel="nofollow">inferred from the size of the effect, while statistical significance is in" rel="nofollow">inferred from the precision of the estimate. The effect size reflected in" rel="nofollow">in the odds ratio in" rel="nofollow">in the first case was 0.08, or an 8% difference in" rel="nofollow">in likelihood of a cold based on vitamin" rel="nofollow">in C supplementation (small practical difference of 8%, but statistically significant, with a p-value of 0.04). The effect size in" rel="nofollow">in the second case was 0.6, or a 60% difference in" rel="nofollow">in the likelihood of a diagnosis of high blood pressure based on stress (large practical difference of 60%, but small statistical significance, with a p-value of 0.07). Consider effect size and how to determin" rel="nofollow">ine whether practical or statistical significance exists. Select a public health research study from one of the peer-reviewed journal articles listed in" rel="nofollow">in the Week 2 Resources. Consider whether effect size is practically or statistically significant in" rel="nofollow">in the study. References Daniel, WW & Cross, CL. (2013). Biostatistics: A Foundation for Analysis in" rel="nofollow">in the Health Sciences. Hoboken, NJ: Wiley. Mendall, M. A., Goggin" rel="nofollow">in, P. M., Molin" rel="nofollow">ineaux, N., Levy, J., Toosy, T., Schrachan, D. … Norfield, T. C. (1994). Relation of Helicobacter pylori in" rel="nofollow">infection and coronary heart disease. British Heart Journal, 71(5), 437–439. Cui, H., Cruz-Corea, M., Giardiello, F. M., Hutcheon, D. F., Kafoneck, D. R., Brandenburg, S. … Fienberg, A. P. (2003). Loss of IGF2 imprin" rel="nofollow">intin" rel="nofollow">ing: A potential marker of colorectal cancer risk. Science, 299(5613),1753–1755. Ford, E. S. (1999). Body mass in" rel="nofollow">index, diabetes, and C-reactive protein" rel="nofollow">in among U.S. adults. Diabetes Care, 22(12), 1971–1977.