HARKing: Hypothesizing After the Results are Known

By L.. 2002

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Citations in the corpus

Strengthening statistical usage in marine ecology (2012, methodology): “This practice has been called HARKing: Hypothesizing After the Results are Known, and has been shown to be quite widespread in most disciplines (Kerr, 1998).”
The preregistration revolution (2018, background): “The problem with this is understood as post hoc theorizing or hypothesizing after the results are known (12).”
Answers to 18 Questions About Open Science Practices (2018, unknown): “…corresponding hypotheses, presenting post hoc hypotheses and analyses that are statistically significant as if they were planned a priori (HARKing; Kerr, 1998; O'Boyle et al., 2017), as well as “cherry-picking” fit indices and/or conducting post hoc analyses (e.g., based on model modification…”
“Positive” Results Increase Down the Hierarchy of the Sciences (2010, background): “In this latter case, therefore, a bias against non-significant results could generate an increase in positive results only if researchers in softer sciences tested more generic hypotheses (for example, ‘‘x is correlated to y’’ or ‘‘x influences y’’ instead of ‘‘x is positively correlated to y’’ or ‘”
Facts Are More Important Than Novelty (2014, background): “…1995), low reliability among peer reviewers (e.g., Cicchetti, 1991; Cole, 1992; D. Peters & Ceci, 1982), hypothesizing after the results are known (e.g., N. Kerr, 1998; Makel, 2014; Maxwell, 2004), misuse of statistical tests and results (e.g., Bakker & Wicherts, 2011: Kruskal & 545513 EDRXXX10.”
Harking, Sharking, and Tharking (2017, background): “Many now believe (Bosco, Aguinis, Field, Pierce, & Dalton, 2015) and, in fact, have long believed (Kerr, 1998), that some of these false leads are directly attributable to Harking.”
Modeling Longitudinal Data from Research on Adolescence (2009, background): “Too often researchers succumb to the tendency to “ Label After Results Are Known ” (LARKing; c.f. HARKing, Kerr, 1998 ).”
A Call for Examining Replication and Bias in Special Education Research (2014, background): “Researchers may also use very weak comparison conditions (i.e., strawman effects) or formulate hypotheses after results are known (i.e., HARKing; Kerr, 1998) to inflate effects.”
The natural selection of bad science (2016, background): “Interpretation of results after data is collected can also generate false positives through biased selection of statistical analyses (Gelman & Loken, 2014) and post-hoc hypothesis formation (Kerr, 1998).”
Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, background): “Fourth, researchers not only look at the effects they were interested in at the outset of their study, but they tend to interpret all statistically significant effects and sometimes even rephrase their hypotheses on the basis of the data obtained (a phenomenon known as harking – hypothesizing after ”
The need for Bayesian hypothesis testing in psychological science (2017, methodology): “To counteract hindsight bias and HARKing (Hypothesizing After the Results are Known; De Groot, 1956/2014; Kerr, 1998), Mike first preregisters each experiment on the Open Science Framework (Open Science Collaboration, 2012), detailing in advance his entire analysis plan including criteria for exclud”
Hidden Multiplicity in Multiway ANOVA: Prevalence, Consequences, and Remedies (2014, unknown): “esearcher specifies H 0 in purely statistical terms (e.g., equality of means) without providing an alternative hypothesis in substantive terms (e.g., women are more depressed than men). Additionally, [21] notes that researchers in psychology are quite commonly seduced into presenting a post hoc hypo”
Replication, statistical consistency, and publication bias. (2013, background): “, 2012) such as optional stopping, multiple testing, subject dropping, and hypothesizing after the results are known (HARKing; Kerr, 1998).”