Registry · Priors

construct.demographic_diversity correlates construct.team_performance

normal · informative · 4 studies · N = 8,907

Distribution

Intervals

Confidence interval (95%) — uncertainty about the mean ρ

[-0.03, 0.01]

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=4, replication: meta-analytic); no primary-deployment evidence yet

• replication: meta-analytic
• aging literature (freshness=0.47)

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 | -0.00971158, 0.0105386);

beta = pm.Normal("beta", mu=-0.00971158, sigma=0.0105386)

brms::prior(normal(-0.00971158, 0.0105386), class = "b")

# base R sample
rnorm(N, mean = -0.00971158, sd = 0.0105386)

np.random.normal(loc=-0.00971158, scale=0.0105386, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

informative

Replication status

meta-analytic

k_studies

4

n_total

8,907

Last updated

2026-05-30T23:51:58.089Z

Quality distribution

Contributing effect sizes

• effect.e62fdc92680619c2
• effect.dcebd1eeb5ff42bb
• effect.d537052e2955dc6c
• effect.8b1e4b7427ec836f

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