Registry · Priors

construct.emotional_exhaustion predicts construct.task_performance

normal · weakly_informative · 2 studies · N = 100

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.343, -0.0428]; mean ≈ -0.193.-0.500-0.346-0.193-0.03970.114z0density

mean ≈ -0.193 · 95% CI ≈ [-0.343, -0.0428]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.336, -0.0459]; mean ≈ -0.191.-0.486-0.339-0.191-0.04290.105r0density

mean ≈ -0.191 · 95% CI ≈ [-0.336, -0.0459]

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
[-0.33, -0.04]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.19, -0.19]
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=-0.19 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.19k = 2 · N = 100

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 | -0.193074, 0.0766965);
beta = pm.Normal("beta", mu=-0.193074, sigma=0.0766965)
brms::prior(normal(-0.193074, 0.0766965), class = "b")
# base R sample
rnorm(N, mean = -0.193074, sd = 0.0766965)
np.random.normal(loc=-0.193074, scale=0.0766965, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.1931, sigma = 0.07670, r_mean = -0.1907, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
2
n_total
100
Last updated
2026-05-30T22:23:31.708Z

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.