ANOVA) Can Best Be Described as Statistical

¶ … ANOVA) can best be described as statistical analysis of data with a variety of different variables and variations and is oftentimes used in a medical and professional research and practices environment. Using ANOVA in research is a commonplace technique, but there are some disadvantages to this usage; the application of the analysis is often conducted in applications where it is inappropriate (Patel, Naik, Patel, 2015).

Ensuring that ANOVA is used in the appropriate manner, and that it is applied in situations that call out for ANOVA requires understanding and comprehension exactly what ANOVA does and how it is used in research. The Patel (2015) concluded that "conceptual understanding and application of ANOVA and Post hoc test was lacking among bio-medical research" (p. 117).

Developing the required understanding will assist the researcher(s) in implementing data analysis that will create a more valid and reliable study, along with results that can be replicated in a similar environment, which is oftentimes described as a goal and objective for many medical studies. The Cleland article presents a study on the three manual physical therapy techniques used in a study of participants suffering from low back pain. Analyzing data with ANOVA seems to be a perfectly fine example of the correct manner in which to implement analysis.

It is a good example because there are a number of variables and variations in the study that can influence the results and ANOVA can analyze those situations in a clear, concise and comprehensive manner. Additionally, the ANOVA analysis model allows the researcher to divide responses into independent sub-vectors or categories. ANOVA is also good in a lot of practical situations where "experimental designs often yield unbalanced data which have different number of observations per unit" (Wang, Sun, 2013, p. 1104).

Primary Analyzing the data should always take into consideration how strong the differences between locales will be in the study. In this particular study the plan anticipates a 93% power to detect any differences in the primary outcome as compared to only an 83% power to detect the differences at a solitary location. This is a difference that is important to the study results since it determines a fluctuation of almost 11%; which is quite a high variance over four locations.

Power differences can best be described as the statistical significance of certain events taking place when an intervention in research is being introduced. Obtaining reliable data of significant events will oftentimes be measured by a power percentage; in this study four locations will be observed and a measurement of 93% power to determine differences of the variations will be observable. Comparing the 83% power difference of just one location to the 93% power should provide the researcher with a much more valid study (at the 93% rate) than is accessible at the 83% rate.

As one recent study (Mi, Di, 2015) determined "testing differential expression can often be reduced to testing that one or more of the regression coefficients equal zero" (p. 3). If the Mi et al. study is true, then the researcher(s) in this specific.