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Activity Number: 212636
Type: Professional Development
Date/Time: Monday, August 1, 2016 : 8:00 AM to 12:00 PM
Sponsor: Section on Statistical Education
Abstract #321878
Title: Bootstrap Methods and Permutation Tests for Doing and Teaching Statistics (ADDED FEE)
Author(s): Tim Hesterberg*
Companies: Google

Early in Stat 101, we teach that robustness is important. Yet later in the course, and too often in practice, we ignore those lessons and use simple means and least-squares regression together with Normal-based inferences, even though the corresponding assumptions are violated. Bootstrapping and permutation tests (BPT) let us check the accuracy of common procedures; they are surprisingly inaccurate in the presence of skewness. BPT offer better alternatives, but we need to know what we're doing-the most common bootstrap methods are less accurate than a t-interval for small n. BPT let us more easily do inferences for a wider variety of statistics (e.g., trimmed means, robust regression) and data collected in a variety of ways (e.g., stratification). We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm. BPT provide output we may graph in familiar ways (like histograms) to help students and clients understand sampling variability, standard errors, p-values, and the Central Limit Theorem (CLT)-not just in the abstract, but for the data set and statistic at hand. This course is intended for teachers and practicing statisticians. No familiarity with these methods is assumed.

Authors who are presenting talks have a * after their name.

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