The File Drawer Problem and Tolerance for Null Results.¶
Rosenthal, R. (1979). The File Drawer Problem and Tolerance for Null Results. Psychological Bulletin, 86(3), 638-641.
Cited by¶
8 citations across 8 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Gatekeeping
- The conditioning invariant is the load-bearing consequence: the published literature is the submission stream conditioned on passing the gate, so it differs from the unconditional stream on every dimension the criteria correlate with — which is the structural root of publication bias (positive results pass more readily than null results, so the published record over-represents positive findings).
This sourceEstablishes publication bias: positive results pass the publication gate more readily than null results, so the published record (the admitted set) over-represents positive findings — the file-drawer problem.
Supported in partVerified against the publisher's abstract
Rosenthal's abstract states the published record is a biased sample of the studies actually carried out — the file-drawer problem — backing the publication-bias half of the claim only.
“Both behavioral researchers and statisti-cians have long suspected that the studies published in the behavioral sciences are a biased sample of the studies that are actually carried out (Bakan, 1967; McNemar, 1960; Smart, 1964; Sterling, 1959).”
- The conditioning invariant is the load-bearing consequence: the published literature is the submission stream conditioned on passing the gate, so it differs from the unconditional stream on every dimension the criteria correlate with — which is the structural root of publication bias (positive results pass more readily than null results, so the published record over-represents positive findings).
- Silence as Signal
- Publication and file-drawer bias. Significant findings are cheap to publish; null results are costly, so the meta-analyst reads the published record as the population of conducted studies and infers effects the full conducted set would not support.
Supported in partVerified against the publisher's abstract
Rosenthal's abstract states the file-drawer premise directly — that published studies are a biased sample of those conducted — but does not state the meta-analyst's inference from the published record.
“the studies published in the behavioral sciences are a biased sample of the studies that are actually carried out”
- Publication and file-drawer bias. Significant findings are cheap to publish; null results are costly, so the meta-analyst reads the published record as the population of conducted studies and infers effects the full conducted set would not support.
- Structural Filtering
- The pattern recurs in academic publishing (funder preferences, journal criteria, peer-review norms, tenure incentives, and field conventions filtering which research reaches print, yielding file-drawer effects and methodological homogeneity),
Supported in partVerified against the publisher's abstract
Rosenthal 1979 supplies the file-drawer problem itself (publication bias from unreported null results in the behavioral sciences), but none of the claim's five institutional filtering mechanisms or methodological homogeneity.
“Both behavioral researchers and statisti-cians have long suspected that the studies published in the behavioral sciences are a biased sample of the studies that are actually carried out (Bakan, 1967; McNemar, 1960; Smart, 1964; Sterling, 1959).”
- The pattern recurs in academic publishing (funder preferences, journal criteria, peer-review norms, tenure incentives, and field conventions filtering which research reaches print, yielding file-drawer effects and methodological homogeneity),
- Unreliable Narrator
- Journalism and source evaluation — explicit models of source motive, proximity, and incentive, with triangulation across independent sources. Leadership and organisational reporting — status reports are systematically optimistic; skilled executives apply known transformations ("on track with a few yellow items" means red) rather than reading reports at face value. Scientific publication — the file-drawer effect, p-hacking, and conflict-of-interest filters make the literature a systematically distorted reporting function; meta-analysts apply explicit corrections.
Supported in partVerified against the publisher's abstract
Rosenthal's abstract documents the file-drawer problem — published studies are a biased sample of all studies — but not p-hacking, conflict-of-interest filters, or meta-analytic corrections.
“Both behavioral researchers and statisti-cians have long suspected that the studies published in the behavioral sciences are a biased sample of the studies that are actually carried out (Bakan, 1967; McNemar, 1960; Smart, 1964; Sterling, 1959).”
- Journalism and source evaluation — explicit models of source motive, proximity, and incentive, with triangulation across independent sources. Leadership and organisational reporting — status reports are systematically optimistic; skilled executives apply known transformations ("on track with a few yellow items" means red) rather than reading reports at face value. Scientific publication — the file-drawer effect, p-hacking, and conflict-of-interest filters make the literature a systematically distorted reporting function; meta-analysts apply explicit corrections.
Domain-specific¶
- File Drawer Problem
- The file drawer problem is the meta-research phenomenon, named by Robert Rosenthal in 1979, in which studies with null, negative, or unsurprising results disproportionately go unpublished — sitting in the researcher's file drawer — while studies with statistically significant or publication-worthy results enter the literature, making the published record a systematically biased sample of the conducted research
Supported in partVerified against the publisher's abstract
“Rosenthal & Gaito, 1963, 1964”
- The file drawer problem is the meta-research phenomenon, named by Robert Rosenthal in 1979, in which studies with null, negative, or unsurprising results disproportionately go unpublished — sitting in the researcher's file drawer — while studies with statistically significant or publication-worthy results enter the literature, making the published record a systematically biased sample of the conducted research
Mechanisms¶
- Detection Power Checklist
- In evidence synthesis the classic masking mode it must not let slide is publication bias, the file-drawer problem
This sourceDefines the file-drawer problem as selective nonpublication of null results, making the visible literature a biased sample of completed studies.
- In evidence synthesis the classic masking mode it must not let slide is publication bias, the file-drawer problem
- Longitudinal Adverse-Plasticity Registry
- A voluntary registry fills with successes and buries the nulls — the file-drawer problem operating at institutional scale,
This sourceDefines the file-drawer problem as the nonpublication of null results and shows how it biases the body of available findings.
- A voluntary registry fills with successes and buries the nulls — the file-drawer problem operating at institutional scale,
- Negative-Case Conservation
- This is the file-drawer problem
This sourceDefines the file-drawer problem as the accumulation of conducted but unreported studies, especially nonsignificant results.
- This is the file-drawer problem
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 5 of 8 citations: 5 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Was it audited? Yes. A second, independent pass read the citation against the article text and recorded a verdict.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
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