ArticleRoyal Society open science2021
The multiplicity of analysis strategies jeopardizes replicability: lessons learned across disciplines.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- When to Adjust for Multiple Testing: A Unifying Guiding Principle.Biometrical journal. Biometrische Zeitschrift · 2026Article
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- The Systematic Multiverse Analysis Registration Tool for defining multiverse analyses.Royal Society open science · 2025Article
- ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.Statistics in medicine · 2025Article
- fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience.Communications biology · 2025Article
- Assessing Covariate Balance With Small Sample Sizes.Statistics in medicine · 2025Article
- Assessing Covariate Balance with Small Sample Sizes.medRxiv : the preprint server for health sciences · 2025Article
- HCP Multi-Pipeline: a derived dataset to investigate analytical variability in fMRI.Scientific data · 2025Article
- Marital status and risk of cardiovascular disease - a multi-analyst study in epidemiology.European journal of epidemiology · 2025Article
- To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism.Biometrical journal. Biometrische Zeitschrift · 2025Article
- The null hypothesis significance test and the dichotomization of the p-value: Errare Humanum Est.Revista peruana de medicina experimental y salud publica · 2025Review
- Article
- Addressing researcher degrees of freedom through minP adjustment.BMC medical research methodology · 2024Article
- Raising awareness of uncertain choices in empirical data analysis: A teaching concept toward replicable research practices.PLoS computational biology · 2024Article
- Big little lies: a compendium and simulation ofRoyal Society open science · 2023Article
- Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- One data set, many analysts: Implications for practicing scientists.Frontiers in psychology · 2023Article
- Article
- Exploring the Multiverse of Analytical Decisions in Scaling Educational Large-Scale Assessment Data: A Specification Curve Analysis for PISA 2018 Mathematics Data.European journal of investigation in health, psychology and education · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process.
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Registered trials
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