Why Most Published Research Findings Are False.¶
Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLoS Medicine, 2(8).
Cited by¶
10 citations across 10 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Epistemic Humility
- Scientific methodology: Designing experiments to falsify rather than confirm (the Popperian 1959 approach), accepting refutation and updating models, reporting confidence intervals and uncertainty quantification, publishing null results, recognizing publication bias and the replication crisis Ioannidis (2005) named in his analysis of why most published research findings are false.
This sourceArgues that low power, small effect sizes, flexible analysis, and bias make most positive findings non-replicable. SUPPORTS marker 216 (replication crisis / why most published research findings are false).
- Scientific methodology: Designing experiments to falsify rather than confirm (the Popperian 1959 approach), accepting refutation and updating models, reporting confidence intervals and uncertainty quantification, publishing null results, recognizing publication bias and the replication crisis Ioannidis (2005) named in his analysis of why most published research findings are false.
- Garbage In, Garbage Out
- In scientific research, derived results inherit primary-data quality, and the replication crisis is in part a GIGO story.
This sourceArgues that small studies, weak designs, bias, and selective reporting limit the reliability of primary findings, a structural driver of the replication crisis.
- In scientific research, derived results inherit primary-data quality, and the replication crisis is in part a GIGO story.
- Hypothesis Testing (Null vs. Alternative)
- The frameworks agree in many applied settings (Bayesian analysis with diffuse priors often produces similar inferences to frequentist) but differ conceptually in what "probability" attaches to—long-run error rates in frequentism; degrees of belief in Bayesianism
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- The frameworks agree in many applied settings (Bayesian analysis with diffuse priors often produces similar inferences to frequentist) but differ conceptually in what "probability" attaches to—long-run error rates in frequentism; degrees of belief in Bayesianism
- Parallel Independent Inspection
- In science, peer review, replication studies, multiple-author meta-analyses, and post-publication review distribute the inspection of a single artifact across diverse inspectors.
This sourcePeer review and replication as distributed inspection, and how correlated bias (a shared confound) degrades it.
- In science, peer review, replication studies, multiple-author meta-analyses, and post-publication review distribute the inspection of a single artifact across diverse inspectors.
- Reproducibility & Replicability
- The three-stage verification tells a clear story: (α) The original pattern reproduces in held-out data (not a spurious artifact), but (β) selection bias explains much of the observed effect (customers adopting the feature combination were pre-selected on lower-default-risk characteristics), and (γ) randomized promotion produces much smaller causal effects than observational analysis suggested
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- The three-stage verification tells a clear story: (α) The original pattern reproduces in held-out data (not a spurious artifact), but (β) selection bias explains much of the observed effect (customers adopting the feature combination were pre-selected on lower-default-risk characteristics), and (γ) randomized promotion produces much smaller causal effects than observational analysis suggested
- Selection Bias
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- Speculative Bubble
- Science: Speculative research fashions, where a popular hypothesis or method attracts disproportionate funding, citations, and effort because it is already attracting them, then deflates when results fail to keep pace with expectation—a reflexive inflow of scholarly attention that overshoots the evidential base, a pattern Ioannidis (2005) connects to inflated false-positive rates in fields where investigative attention rushes ahead of replication.
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- Science: Speculative research fashions, where a popular hypothesis or method attracts disproportionate funding, citations, and effort because it is already attracting them, then deflates when results fail to keep pace with expectation—a reflexive inflow of scholarly attention that overshoots the evidential base, a pattern Ioannidis (2005) connects to inflated false-positive rates in fields where investigative attention rushes ahead of replication.
- Statistical Power
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- Statistical Significance (p-Value)
- The persistent misinterpretation of p-values as posterior probabilities is not a failure of education alone—it reflects that the Bayesian quantity is often the decision-relevant one while the frequentist p-value is the more easily computed
This sourceFoundational analysis of how publication bias, low statistical power, and flexible analytic choices produce a literature in which most positive findings fail to replicate—motivating epistemic humility about scientific claims.
- The persistent misinterpretation of p-values as posterior probabilities is not a failure of education alone—it reflects that the Bayesian quantity is often the decision-relevant one while the frequentist p-value is the more easily computed
- Technical Debt
Verification¶
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