Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference

EMAC 2020 Annual Conference

The Filter Curve: Toward a Measure to Prevent p-Hacking

Published: May 27, 2020


Florian Dost, University of Manchester; Lennard Schmidt, HHL - Leipzig Graduate School of Management; Erik Maier, HHL Leipzig Graduate School of Management


p-hacking; filter curve; Mechanical Turk


Many empirical studies filter participants (e.g., for incorrect attention checks or quick responses), especially when using participant pools such as Amazon MTurk. Yet, there is no consensus on whether and how to filter. This might originate from different perspectives on filtering participants: it may be evaluated positively (e.g., as it might be necessary to prevent inattentive participants from biasing results) or negatively (e.g., as it may enable p-hacking). This research aims to bridge these opposites: first, we empirically compare the effects of different filters and filter levels on validity, reliability, power and effects sizes of the results. Second, we introduce the Filter Curve and our R-package “FiltR” as a means to recognize filtering which might be used to p-hack results. We suggest that filtering is not per se bad – although some filters decrease reliability and validity – but that researchers should be transparent in how sensitive results are for different filter combinations.