Registry · Priors

construct.employment_interview predicts construct.task_performance

normal · weakly_informative · 3 studies · N = 55,940

Distribution

Storage scale (Fisher z)

mean ≈ 0.352 · 95% CI ≈ [0.278, 0.426]

Reader scale (r)

mean ≈ 0.338 · 95% CI ≈ [0.273, 0.403]

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: [0.27, 0.40]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.24, 0.43]
I² (heterogeneity) — share of total variance from between-study differences: 98% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.34 (k=3, high heterogeneity (I²=0.98)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.34	k = 3 · N = 55,940

high heterogeneity (I²=0.98)
replication: meta-analytic

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.

StanPyMCbrmsRNumPy

target += normal_lpdf(beta | 0.351932, 0.0376406);

beta = pm.Normal("beta", mu=0.351932, sigma=0.0376406)

brms::prior(normal(0.351932, 0.0376406), class = "b")

# base R sample
rnorm(N, mean = 0.351932, sd = 0.0376406)

np.random.normal(loc=0.351932, scale=0.0376406, size=N)

Parameters

Family	Parameters
normal	I2 = 0.9768, mu = 0.3519, sigma = 0.03764, r_mean = 0.3381, k_studies = 3.000, tau_squared = 0.003031, fisher_z_bias = 0.006447, r_mean_bare_bones = 0.3316

Synthesis

Method: random_effects_meta
Informativeness: weakly_informative
Replication status: meta-analytic
k_studies: 3
n_total: 55,940
Last updated: 2026-07-16T01:07:40.925Z

Quality distribution

Grade	Count
A	2
B	1
C	0
D	0

Source articles

The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).

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.