Statistical Power Analysis for the Behavioral Sciences¶
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.
Cited by¶
9 citations across 9 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Effect Size
- The standardized d metric rapidly became the foundational unit for meta-analyses and effect-size syntheses across psychology
This sourceLawrence Erlbaum Associates. Codifies standardized effect-size definitions (d, r, f, w, h, q) and the small/medium/large (0.2/0.5/0.8) conventions offered as rough benchmarks — directly supports marker 071 on the Cohen's d framework.
- The standardized d metric rapidly became the foundational unit for meta-analyses and effect-size syntheses across psychology
- Experimental Design
- Designers should specify the minimum effect size of practical importance and power the study to detect that effect reliably, a discipline Cohen (1988) systematized in Statistical Power Analysis for the Behavioral Sciences.
This sourceFoundational text on power analysis linking sample size, effect size, significance threshold, and noise level into a coherent design discipline; grounds the prime's claim that designers should specify a minimum effect size of practical importance and power the study to detect it.
Supported in partVerified against the work's full text
Publisher's description shows Cohen's book is a power-analysis guide for research planning covering effect size, but not the claim's normative minimum-effect-size prescription.
“Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying"...”
- Designers should specify the minimum effect size of practical importance and power the study to detect that effect reliably, a discipline Cohen (1988) systematized in Statistical Power Analysis for the Behavioral Sciences.
- Hypothesis Testing (Null vs. Alternative)
- Is the analysis pre-registered, with primary and secondary hypotheses distinguished and multiplicity adjustment specified
This sourceLawrence Erlbaum Associates. Foundational text on power analysis: links sample size, effect size, significance threshold, and noise level into a coherent design discipline — the practical instantiation of "set decision thresholds appropriate to the noise level" for empirical research.
- Is the analysis pre-registered, with primary and secondary hypotheses distinguished and multiplicity adjustment specified
- Measurement Uncertainty and Observational Noise
- It supports disciplined decision-making despite noisy data: don't act on noise alone; aggregate multiple measurements to average out noise; set decision thresholds appropriate to the noise level; design experiments to be robust to expected noise levels; use statistical methods to separate signal from noise, a discipline Cohen (1988) formalizes through statistical power analysis for the behavioral sciences.
This sourceLawrence Erlbaum Associates. Foundational text on power analysis: links sample size, effect size, significance threshold, and noise level into a coherent design discipline — the practical instantiation of "set decision thresholds appropriate to the noise level" for empirical research.
Supported in partVerified against the publisher's abstract
The book is a guide to power analysis relating effect size, sample size and significance threshold, which backs the threshold clause but not the claim's averaging-out-noise or design clauses.
“Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis.”
- It supports disciplined decision-making despite noisy data: don't act on noise alone; aggregate multiple measurements to average out noise; set decision thresholds appropriate to the noise level; design experiments to be robust to expected noise levels; use statistical methods to separate signal from noise, a discipline Cohen (1988) formalizes through statistical power analysis for the behavioral sciences.
- Statistical Power
- Power analysis is the pre-specified planning discipline that prevents the twin failures of underpowering (running studies that cannot detect meaningful effects, producing inconclusive results and wasted resources) and overpowering (running studies much larger than needed, detecting trivial effects as significant while consuming excessive resources)
This sourceLawrence Erlbaum Associates. Foundational text on power analysis: links sample size, effect size, significance threshold, and noise level into a coherent design discipline — the practical instantiation of "set decision thresholds appropriate to the noise level" for empirical research.
Supported in partVerified against the work's full text
Supplies the foundational power-analysis framework for applied statistics, but says nothing about its transfer to education, medicine, or the other domains named.
“Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis.”
- Power analysis is the pre-specified planning discipline that prevents the twin failures of underpowering (running studies that cannot detect meaningful effects, producing inconclusive results and wasted resources) and overpowering (running studies much larger than needed, detecting trivial effects as significant while consuming excessive resources)
- Statistical Significance (p-Value)
- Physics and particle physics: The 5σ convention (p ≈ 3×10⁻⁷ one-sided) reflects the extraordinary-evidence standard for fundamental-physics discovery claims
This sourceLawrence Erlbaum Associates. Foundational text on power analysis: links sample size, effect size, significance threshold, and noise level into a coherent design discipline — the practical instantiation of "set decision thresholds appropriate to the noise level" for empirical research.
- Physics and particle physics: The 5σ convention (p ≈ 3×10⁻⁷ one-sided) reflects the extraordinary-evidence standard for fundamental-physics discovery claims
- Type I & Type II Errors
- In medicine and diagnostics, diagnostic test evaluation frames sensitivity (1−β, probability of detecting disease when present) and specificity (1−α, probability of correct negative when disease absent); clinical trial design uses formal α and β targets (commonly α = 0.05 two-sided and β = 0.20 for Phase 3)
This sourceLawrence Erlbaum Associates. Foundational text on power analysis: links sample size, effect size, significance threshold, and noise level into a coherent design discipline — the practical instantiation of "set decision thresholds appropriate to the noise level" for empirical research.
- In medicine and diagnostics, diagnostic test evaluation frames sensitivity (1−β, probability of detecting disease when present) and specificity (1−α, probability of correct negative when disease absent); clinical trial design uses formal α and β targets (commonly α = 0.05 two-sided and β = 0.20 for Phase 3)
Mechanisms¶
- Null-Result Power Check
- It is the operational face of statistical power: a low-power null carries almost no information, and confusing it for evidence of absence is a Type II error dressed as a discovery.
This sourceExplains that a nonsignificant result does not by itself establish the absence of an effect.
- It is the operational face of statistical power: a low-power null carries almost no information, and confusing it for evidence of absence is a Type II error dressed as a discovery.
- Standardized Mean Difference Calculation
- Restrict the range of who you study — a selective, homogeneous sample — and the SD shrinks, so the same raw improvement reports a larger standardized effect.
This sourceDefines standardized mean difference as a raw mean difference divided by a standard deviation, so a smaller denominator yields a larger standardized effect for the same raw difference.
- Restrict the range of who you study — a selective, homogeneous sample — and the SD shrinks, so the same raw improvement reports a larger standardized effect.
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 3 of 9 citations: 3 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 3 other ways.
- https://search.worldcat.org/title/Statistical-power-analysis-for-the-behavioral-sciences/oclc/17877467 ×1
- https://www.routledge.com/Statistical-Power-Analysis-for-the-Behavioral-Sciences/Cohen/p/book/9780805802832 ×1
- https://www.routledge.com/Statistical-Power-Analysisfor-the-Behavioral-Sciences/Cohen/p/book/9780805802832 ×1
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