ArticleAmerican journal of audiology2026
Effect Sizes and Statistical Power in Hearing Aid Research.
Article in American journal of audiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundEffect sizes quantify the magnitude of group differences, yet hearing aid research still relies on Cohen's benchmarks (0.20, 0.50, 0.80). These cutoffs are not field specific and may misrepresent typical effects.
objectiveThe aims of this study were to characterize the distribution of effect sizes in adult hearing aid research and use these data to estimate sample sizes required to achieve varying levels of statistical power.
methodA systematic search of PubMed, CINAHL, and Embase identified English-language randomized controlled trials (RCTs) involving adults with mild-to-severe hearing loss using commercially available air-conduction hearing devices. Eligible outcomes included standardized self-reports and behavioral measures. Absolute Hedges's
resultsFrom 15,066 records, 33 trials (4,471 participants) met the inclusion criteria; of these, 17 trials provided 63 effect sizes. Across all outcomes, the 25th, 50th, and 75th percentile values (Hedges's
conclusionsThe empirical distribution of effect sizes in hearing aid RCTs is shifted toward smaller numerical values relative to Cohen's conventional benchmarks. Therefore, using Cohen's generic benchmarks makes an effect appear smaller than it is in the context of this field. We recommend using the effect sizes of 0.1, 0.2, and 0.5 for small, medium, and large effect sizes, respectively, when interpreting hearing aid trial results. Adoption of these empirically derived benchmarks will improve the accuracy of interpretation, guide more realistic sample size planning, and enhance the replicability of future trials. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.32559894.
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