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Record W2897004045 · doi:10.29173/cons29342

A Review of Rozeboom’s Ideas with an Analysis of Issues in Null Hypothesis Significance Testing

2018· review· en· W2897004045 on OpenAlexvenueno aff
Lexi Brunner

Bibliographic record

VenueConstellations · 2018
Typereview
Languageen
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsnot available
Fundersnot available
KeywordsPublicationContext (archaeology)Replication (statistics)PsychologySignificance testingNull hypothesisEpistemologyExperimental psychologyEngineering ethicsLawMedicinePolitical scienceCognitionEngineeringHistoryPhilosophyEconometrics

Abstract

fetched live from OpenAlex

Reexamining William Rozeboom’s recommendations for the future direction of disciplines such as psychology and philosophy is imminent due to the pressing issues in null hypothesis significance testing (NHST). An overreliance on NHST forms the basis of the replication crisis in psychology. Likewise, the discipline’s stringent guidelines on significance levels convey a pressure to publish, which is also significantly contributing to the replication crisis. As researchers’ careers are staked on the extent to which they publish, reassessing the fundamental issues with NHST within the context of Rozeboom’s ideas is paramount. In this brief review, I focus specifically on Rozeboom’s critiques of NHST and on a proposed alternative for evaluating the depth of findings within the framework of theoretical psychology. Thereafter, I compare his foreboding concerns with modern issues facing the validity of psychology and posit Rozeboom prophesized the failings of the discipline years ahead of his time. Keywords: Theoretical Psychology; NHST; William Rozeboom; P-Hacking; Explanatory Induction

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.014
Science and technology studies0.0020.016
Scholarly communication0.0040.008
Open science0.0030.002
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.218
GPT teacher head0.332
Teacher spread0.114 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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