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- W4248189981 abstract "Theories are a key part of the scientific process. How should we evaluate theories against evidence? The two traditional and most widespread approaches use single studies and qualitative reviews to evaluate theories; more recently, large-scale replications entered the picture. We argue here that none of these approaches fits in with cumulative science tenets. We propose instead Community-Augmented Meta-Analyses (CAMAs) which are cumulative and open, built using all available data, with mechanisms in place to prevent data overfitting and to statistically model methodological noise. When using CAMAs as the basis for theory evaluation, it becomes a transparent, replicable endeavor. We provide step-by-step recommendations for how to implement this approach -- and what it means when one cannot. This leads us to conclude that CAMAs highlight areas of uncertainty better than the alternative theory evaluation approaches, and can trigger a much needed shift towards a cumulative mindset both with respect to theory and data, leading us to do experiments and qualitative reviews differently." @default.
- W4248189981 created "2022-05-12" @default.
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- W4248189981 date "2020-07-01" @default.
- W4248189981 modified "2023-10-14" @default.
- W4248189981 title "A meta-analytic approach to evaluating the explanatory adequacy of theories" @default.
- W4248189981 doi "https://doi.org/10.31219/osf.io/83kg2" @default.
- W4248189981 hasPublicationYear "2020" @default.
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