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Activity Number: 352
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #319795 View Presentation
Title: Adjusting Published Estimates for Exploratory Biases Using the Truncated Normal Distribution
Author(s): Travis Loux* and Orlando Davy
Companies: Saint Louis University and Saint Louis University
Keywords: hypothesis testing ; data fishing ; publication bias

Statisticians have long been aware of the limitations of using estimates from statistically significant hypothesis tests, including multiplicity, publication bias, data fishing or p-hacking, and vanishing effects. These problems have garnered broader attention with recent work showing lack of replicability of studies in a range of applied fields. We present a simple data-based method of adjusting statistically significant estimates by exploiting the relationship between the true sampling distribution and the truncated sampling distribution defined by the rejection region of the hypothesis test. Simulations show the method is an effective, though imperfect, solution to the problem of biases in published research.

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

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