Cramer's V and the other chi-square-family effect sizes are positive under exact independence, so a reported value can be smaller than what the same statistic produces on a table with no association at all. The upward bias is classical (Tschuprow 1925; Bartlett 1937; corrected by Bergsma 2013), and reporting the boundary value, the critical effect size, beside the observed one has been recommended in general form (Perugini et al. 2025); we claim neither. Our contribution is two things a general recommendation does not supply. First, an exact per-table reporting gate: the noise floor V_0.95, the value the effect size reaches by chance 5% of the time under independence, computed from the exact margin-conditional law rather than the asymptotic critical value. The asymptotic floor miscalibrates where reporting is riskiest: on heterogeneous tables its false-alarm rate reaches 0.10 against a 0.05 target, while the exact floor holds nominal. Second, a measurement: across 4,129 real two-way tables from 291 public datasets, 21.8% of labeled effects (cluster-robust 95% CI 17 to 27%) sit on tables with no significant association. We recommend reporting V_0.95, in exact form on sparse or heterogeneous tables, beside every association measure and at the design stage.
Benford's law specifies a marginal distribution for significant digits, whereas the usual first-digit Pearson $p$-value is calibrated under an independent multinomial sampling model. We separate these statements with four constructions that share the same one-time or pooled Benford target but have different joint struc...
Rank-based tests need no distributional assumptions to be valid, but the sample size they require does depend on the shapes of the two outcome distributions, which are unknown at the design stage. Standard practice substitutes a variance calibrated under the null. We ask how wrong that substitution can be. Writing $\Pi...
Eta-squared (η2) is the most widely reported proportion-of-variance effect size in ANOVA, yet its estimators are biased and non-normality is rarely studied. We prove that, for balanced one-way fixed effects ANOVA, no function of the usual F-statistic-the class containing the estimators in routine use-is exactly unbiase...
Daiki Nakamura· British Journal of Mathemati...· 0 citations
Van Aert and Van Assen (2026) introduced a novel method called p-uniform* for estimating average effect size in meta-analyses subject to publication bias. They characterized the method as a distinct, more parsimonious model than the well-known three-parameter selection model (3PSM) of Hedges (1992). I argue that p-unif...
James E. Pustejovsky· Psychonomic Bulletin & Revie...· 0 citations
When many binary items are analyzed, most cells in the complete response table are empty. Full-table statistics may then be unreliable, and an omnibus result does not locate the misfit. This paper develops a local goodness-of-fit testing method for binary item response models by extending the weighted sequential sums-o...
Jin-Hui Xu, Run-Qi Li, M. Reiser· Symmetry· 0 citations
Suppose we want a cutoff that 90% of a population falls below. We estimate it from a sample, and another sample would give a different cutoff and a different fraction below it. We ask how much that fraction varies when observations come in independent groups, such as pupils in classrooms or sentences in news articles....
A. Noonan· 0 citations
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