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      Preregistration: A Solution to Undisclosed Analytic Flexibility in ERP Research

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      Center for Open Science

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          Abstract

          Confirmation bias and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility increases false positive results and inflates effect sizes, ultimately creating a skewed representation of knowledge. This issue is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit undisclosed analytic flexibility is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility (particularly in human neuroimaging and electrophysiology), discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by addressing common rebuttals as well as clarifying possibilities and limitations of this open science practice.

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          (View ORCID Profile)
          Journal
          Center for Open Science
          November 04 2020
          Article
          10.31234/osf.io/4tgve
          0376eea5-2115-4bcb-b852-0fa085f2e2df
          © 2020

          https://creativecommons.org/licenses/by/4.0/legalcode

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