Registry · Priors

construct.voice_behavior predicts construct.task_performance

normal · weakly_informative · 1 studies · N = 3,750

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.356, 0.420]; mean ≈ 0.388.0.3230.3560.3880.4210.454z0density

mean ≈ 0.388 · 95% CI ≈ [0.356, 0.420]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.342, 0.398]; mean ≈ 0.370.0.3140.3420.3700.3980.426r0density

mean ≈ 0.370 · 95% CI ≈ [0.342, 0.398]

Weakly informative prior. This prior is weakly informative. It will nudge your posterior but won't overwhelm it; expect data to do most of the work in modest samples.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.34, 0.40]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
— needs ≥2 studies to estimate (k = 1)
I² (heterogeneity) — share of total variance from between-study differences
— needs ≥2 studies to estimate

k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed

Evidence provenance

published ρ=0.37 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.37k = 1 · N = 3,750

No primary-deployment evidence yet — this prior rests on published literature alone. As anonymized, aggregated effect sizes from real deployments are contributed, they appear here as a distinct, publication-bias-free source, fused with the literature into a posterior estimate.

Code

Drop this prior straight into your model. Snippets generated from the synthesized distribution + parameters.

target += normal_lpdf(beta | 0.388423, 0.0163365);
beta = pm.Normal("beta", mu=0.388423, sigma=0.0163365)
brms::prior(normal(0.388423, 0.0163365), class = "b")
# base R sample
rnorm(N, mean = 0.388423, sd = 0.0163365)
np.random.normal(loc=0.388423, scale=0.0163365, size=N)

Parameters

FamilyParameters
normalmu = 0.3884, sigma = 0.01634, r_mean = 0.3700, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
3,750
Last updated
2026-05-29T15:21:19.393Z

Quality distribution

GradeCount
A1
B0
C0
D0

Contributing effect sizes

Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.