Multiple hypothesis testing in experimental economics

By John A. List, A. Shaikh, Yang Xu. Experimental Economics, 2019

527 citations recorded by the source corpus.

Subfields: General Decision Sciences, Safety Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/94a8e805b93d9f20d7e6fb56877ec897acda970c

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In General Decision Sciences

In Safety Research

In Statistics and Probability

Citations in the corpus

Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments (2019, methodology): “2 We use the closed form formula provided by Lu et al. (2019) for the Type-M error.”
High-dimensional econometrics and regularized GMM (2018, background): “A useful discussion of multiple testing in experimental economics can be found in [88].”
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables (2017, methodology): “We utilized the following multiple hypothesis testing procedure [23] to validate this assumption:”
Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan (2018, unknown): “List et al. (2016) and Bonferonni p-values are calculated controlling for the family-wise error rate for the first five variables reported in the table, and then for each set of variables underlying the index respectively.”
The Behavioralist as Tax Collector: Using Natural Field Experiments to Enhance Tax Compliance (2017, methodology): “proposed by List et al. (2016). Regression (I) shows that the general injunctive norm is not particularly effective in increasing the likelihood of paying tax.”
How to Improve Tax Compliance? Evidence from Population-Wide Experiments in Belgium (2019, methodology): “p-values adjusted for multiple hypothesis testing (List et al., 2016).”
Financial Incentives as Signals: Experimental Evidence from the Recruitment of Village Promoters in Uganda (2019, methodology): “They are also robust to correcting standard errors for Multiple Hypothesis Testing using the Bonferroni, Holm correction or the approach used in List et al. (2016).”
Access to modern energy: a review of barriers, drivers and impacts (2016, background): “Moreover, most studies reviewed do not control or correct for multiple hypothesis testing, which may lead to over-rejection of null hypothesis and overestimation of program impacts (List et al., 2016).9”