Analysis of Means (ANOM) is an underappreciated methodology that has relevance to quality control and institutional comparisons.
Unlike Analysis of Variance (ANOVA), which compares one group mean to another group mean, ANOM compares each group mean to the overall mean. The calculations in ANOM are simple and direct. ANOM also avoids many of the ambiguities inherent in the multiple comparisons used in ANOVA, and avoids a common misinterpretation about overlapping confidence intervals.
In this training, we illustrate the mechanics of calculating ANOM and provide context for when you should or should not use it.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
About the Instructor
Steve Simon works as an independent statistical consultant and as a part-time faculty member in the Department of Biomedical and Health Informatics at the University of Missouri-Kansas City. He has previously worked at Children’s Mercy Hospital, the National Institute for Occupational Safety and Health, and Bowling Green State University.
Steve has over 90 peer-reviewed publications, four of which have won major awards. He has written one book, Statistical Evidence in Medical Trials, and is the author of a major website about Statistics, Research Design, and Evidence Based Medicine, www.pmean.com. One of his current areas of interest is using Bayesian models to forecast patient accrual in clinical trials. Steve received a Ph.D. in Statistics from the University of Iowa in 1982.
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