Registry · Priors

construct.performance_feedback predicts construct.task_performance

normal · weakly_informative · 2 studies · N = 28,904

Distribution

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

[0.08, 0.45]

Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)

[0.03, 0.49]

I² (heterogeneity) — share of total variance from between-study differences

77%

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=0.27 (k=2, high heterogeneity (I²=0.77)); no primary-deployment evidence yet

• high heterogeneity (I²=0.77)
• replication: meta-analytic
• aging literature (freshness=0.41)

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

target += normal_lpdf(beta | 0.281609, 0.102361);

beta = pm.Normal("beta", mu=0.281609, sigma=0.102361)

brms::prior(normal(0.281609, 0.102361), class = "b")

# base R sample
rnorm(N, mean = 0.281609, sd = 0.102361)

np.random.normal(loc=0.281609, scale=0.102361, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

2

n_total

28,904

Last updated

2026-05-30T22:53:16.175Z

Quality distribution

Contributing effect sizes

• effect.9f116dd10554ff0c
• effect.a6985ad16f2151fa

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