# April 2018 Member Webinar: Equivalence Tests and Non-Inferiority

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Statistics is, to a large extent, a science of comparison. You are trying to test whether one group is bigger, faster, or smarter than another.

You do this by setting up a null hypothesis that your two groups have equal means or proportions and an alternative hypothesis that one group is “better” than the other. The test has interesting results only when the data you collect ends up rejecting the null hypothesis.

But there are times when the interesting research question you’re asking is not about whether one group is better than the other, but whether the two groups are equivalent.

For example, at which point do second-language speakers make as few grammar errors as native speakers?

Or at which point does a new drug with fewer side effects work just as well as the standard treatment?

Finding equivalence is impossible, by conventional wisdom. You can’t prove a negative. (Well, you can prove a negative, but it takes more work.)

There is an entirely different hypothesis testing framework that allows for this kind of comparison.

In this webinar, we’ll review the framework of the traditional hypothesis test and compare it to the framework of the equivalence test and its one-sided cousin, the non-inferiority test.

You’ll learn how to specify a range of equivalence or margin of non-inferiority, where those values define differences that are small enough to be tolerable.

You’ll also identify critical issues in the design of an equivalence or non-inferiority study that can make or break your experiment.

Note: This webinar is only accessible to members of the Statistically Speaking Membership Program.

### Date and Time

Wednesday, April 18, 2018
2 pm – 3:30 pm (US EDT) (In a different time zone?)

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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