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Magnus Johannesson

Researcher at Stockholm School of Economics

Publications -  348
Citations -  47273

Magnus Johannesson is an academic researcher from Stockholm School of Economics. The author has contributed to research in topics: Willingness to pay & Population. The author has an hindex of 102, co-authored 342 publications receiving 40776 citations. Previous affiliations of Magnus Johannesson include University of Southern California & Linköping University.

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Estimating the reproducibility of psychological science

Alexander A. Aarts, +290 more
- 28 Aug 2015 - 
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
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Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

James J. Lee, +94 more
- 23 Jul 2018 - 
TL;DR: A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance ineducational attainment and 7–10% ofthe variance in cognitive performance, which substantially increases the utility ofpolygenic scores as tools in research.
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Redefine statistical significance

Daniel J. Benjamin, +76 more
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
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Redefine Statistical Significance

TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
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Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies

TL;DR: It is the responsibility of model developers to conduct modeling studies according to the best practicable standards of quality and to communicate results with adequate disclosure of assumptions and with the caveat that conclusions are conditional upon the assumptions and data on which the model is built.