Registry · Priors

construct.realistic_job_preview predicts construct.met_expectations

normal · informative · 2 studies · N = 4,168

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.0430, 0.0230]; mean ≈ -0.0100.-0.0773-0.0436-0.01000.02360.0573z0density

mean ≈ -0.0100 · 95% CI ≈ [-0.0430, 0.0230]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.0429, 0.0229]; mean ≈ -0.0100.-0.0772-0.0436-0.01000.02360.0572r0density

mean ≈ -0.0100 · 95% CI ≈ [-0.0429, 0.0229]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.04, 0.02]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.01, -0.01]
I² (heterogeneity) — share of total variance from between-study differences
0%

The true effect is ~constant across settings — it generalizes.

SD_ρ≈0 — true effect is ~constant across settings; generalizes (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=-0.01 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.01k = 2 · N = 4,168

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 | -0.0100003, 0.0168128);
beta = pm.Normal("beta", mu=-0.0100003, sigma=0.0168128)
brms::prior(normal(-0.0100003, 0.0168128), class = "b")
# base R sample
rnorm(N, mean = -0.0100003, sd = 0.0168128)
np.random.normal(loc=-0.0100003, scale=0.0168128, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.01000, sigma = 0.01681, r_mean = -0.01000, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
4,168
Last updated
2026-05-30T23:52:00.984Z

Quality distribution

GradeCount
A1
B1
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.