An analysis of variance, otherwise known as an ANOVA, is defined as a grouping of statistical models and their correlated calculation methods employed to analyze the significance of differences among group means within a certain sample (Sullivan, 2019). In the most basic form of this analysis, ANOVA is used as a statistical test of whether two or more group means are equal to one another.
Results of ANOVA analysis
Any area of employment that uses statistical analysis or conducts research will likely need to understand and be capable of completing and understanding the results of an ANOVA analysis. For example, a collection of clinical psychology patients are being treated with three different forms of therapy, which are: clinical counseling, pharmacological treatment and biological feedback. An ANOVA comparison could be employed to determine if one of the treatment methods is optimal compared to the two others. Another example could be a pharmaceutical company that maintains two different procedures for the manufacturing of their specific medications or drugs. The company could use an ANOVA analysis to determine if one manufacturing procedure is more efficient and practical compared to the other. For one final hypothetical example, consider that the Federal Drug Administration (‘FDA’) wants to examine the safety of a newly proposed psychiatric medication, which comes in three forms: orally ingested liquid, orally ingested pills and intravenous application. The FDA could collect a sample of three for each of the forms of medication. Employing an ANOVA analysis could be used to test whether the mean effectiveness over a period of one year is equal for each form of the new medication.
References
Sullivan, L. (2019). Hypothesis Testing – Analysis of Variance (ANOVA). Retrieved June 10,
