A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments
Authors
Keywords
-
Journal
INTERNATIONAL STATISTICAL REVIEW
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2018-06-13
DOI
10.1111/insr.12273
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Stan: A Probabilistic Programming Language
- (2017) Bob Carpenter et al. Journal of Statistical Software
- New concerns raised over value of genome-wide disease studies
- (2017) Ewen Callaway NATURE
- The ASA's Statement onp-Values: Context, Process, and Purpose
- (2016) Ronald L. Wasserstein et al. AMERICAN STATISTICIAN
- 1,500 scientists lift the lid on reproducibility
- (2016) Monya Baker NATURE
- Comment on "Estimating the reproducibility of psychological science"
- (2016) D. T. Gilbert et al. SCIENCE
- Response to Comment on "Estimating the reproducibility of psychological science"
- (2016) C. J. Anderson et al. SCIENCE
- Evaluating replicability of laboratory experiments in economics
- (2016) C. F. Camerer et al. SCIENCE
- Statistics: P values are just the tip of the iceberg
- (2015) Jeffrey T. Leek et al. NATURE
- Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al.
- (2015) Philip T. Reiss NEUROIMAGE
- The cancer test
- (2015) J. Kaiser SCIENCE
- Estimating the reproducibility of psychological science
- (2015) SCIENCE
- What is the question?
- (2015) J. T. Leek et al. SCIENCE
- The Extent and Consequences of P-Hacking in Science
- (2015) Megan L. Head et al. PLOS BIOLOGY
- The Statistical Crisis in Science
- (2014) Andrew Gelman et al. AMERICAN SCIENTIST
- Scientific method: Statistical errors
- (2014) Regina Nuzzo NATURE
- An open investigation of the reproducibility of cancer biology research
- (2014) Timothy M Errington et al. eLife
- Discussion: Comment on a paper by Jager and Leek
- (2013) D. R. Cox BIOSTATISTICS
- Discussion: Difficulties in making inferences about scientific truth from distributions of published p-values
- (2013) A. Gelman et al. BIOSTATISTICS
- An estimate of the science-wise false discovery rate and application to the top medical literature
- (2013) L. R. Jager et al. BIOSTATISTICS
- Discussion: An estimate of the science-wise false discovery rate and application to the top medical literature
- (2013) S. N. Goodman BIOSTATISTICS
- A Survey on Data Reproducibility in Cancer Research Provides Insights into Our Limited Ability to Translate Findings from the Laboratory to the Clinic
- (2013) Aaron Mobley et al. PLoS One
- P-Value Precision and Reproducibility
- (2012) Dennis D. Boos et al. AMERICAN STATISTICIAN
- Replication studies: Bad copy
- (2012) Ed Yong NATURE
- Raise standards for preclinical cancer research
- (2012) C. Glenn Begley et al. NATURE
- Voodoo and circularity errors
- (2012) Edward Vul et al. NEUROIMAGE
- A peculiar prevalence of p values just below .05
- (2012) E.J. Masicampo et al. QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY
- Believe it or not: how much can we rely on published data on potential drug targets?
- (2011) Florian Prinz et al. NATURE REVIEWS DRUG DISCOVERY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started