Registry · Priors

construct.feedback_seeking predicts construct.task_performance

normal · informative · 2 studies · N = 17,051

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.0217, 0.358]; mean ≈ 0.168.-0.219-0.02560.1680.3620.556z0density

mean ≈ 0.168 · 95% CI ≈ [-0.0217, 0.358]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.0180, 0.351]; mean ≈ 0.167.-0.210-0.02180.1670.3550.544r0density

mean ≈ 0.167 · 95% CI ≈ [-0.0180, 0.351]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.02, 0.34]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.10, 0.41]
I² (heterogeneity) — share of total variance from between-study differences
98%

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=0.17 (k=2, high heterogeneity (I²=0.98)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.17k = 2 · N = 17,051

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 | 0.168222, 0.0969178);
beta = pm.Normal("beta", mu=0.168222, sigma=0.0969178)
brms::prior(normal(0.168222, 0.0969178), class = "b")
# base R sample
rnorm(N, mean = 0.168222, sd = 0.0969178)
np.random.normal(loc=0.168222, scale=0.0969178, size=N)

Parameters

FamilyParameters
normalI2 = 0.9843, mu = 0.1682, sigma = 0.09692, r_mean = 0.1667, k_studies = 2.000, tau_squared = 0.01849

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
17,051
Last updated
2026-05-30T19:49:57.895Z

Quality distribution

GradeCount
A2
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.