How to p-hack (and avoid p-hacking) in ERP Research

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Luck, S. J., & Gaspelin, N. (2017). How to Get Statistically Significant Effects in Any ERP Experiment (and Why You Shouldn’t). Psychophysiology, 54, 146-157.

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In this article, we show how ridiculously easy it is to find significant effects in ERP experiments by using the observed data to guide the selection of time windows and electrode sites. We also show that including multiple factors in your ANOVAs can dramatically increase the rate of false positives (Type I errors). We provide some suggestions for methods to avoid inflating the Type I error rate.

This paper was part of a special issue of Psychophysiology on Reproducibility edited by Emily Kappenman and Andreas Keil.

Announcing the Virtual ERP Boot Camp Blog

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We have started a new blog called Virtual ERP Boot Camp on erpinfo.org to provide tips, advice, and other information about best practices for ERP research. We will also be highlighting new research that is relevant to the field.

To submit a question to our advice column, send an email to ERPquestions@gmail.com. We can't answer every question, but we will post answers to questions that we believe will be of general interest.

You can also follow us on our new Twitter account: @erpbootcamp.

Steve and Emily