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Use Bayesian inference, equivalence testing, and multiple operationalizations to make null effects theoretically informative

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Evidence · 4 claims (4 for) · 6 studies (3 theoretical, 2 causal, 1 review), q2–q3 · 2 of 6 report an effect size · 3 claims rest on one study

Description

The article recommends methodological moves for treating nulls as informative rather than inconclusive. "Bayesian inference provides one avenue for quantifying evidence in favor of the null hypothesis," allowing researchers to distinguish absence of evidence from evidence of absence, and equivalence testing enables formal evaluation of whether effects are practically negligible. Including multiple operationalizations of ostensibly similar constructs strengthens inference, as convergent null effects provide more robust constraints than any single test.

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Page metadata
Field Value
type strategy
id bayesian-equivalence-testing-for-null-effects
title Use Bayesian inference, equivalence testing, and multiple operationalizations to make null effects theoretically informative
description The article recommends methodological moves for treating nulls as informative rather than inconclusive.
status review
generated by process:wiki-ingest on 2026-10-21
trust unverified — no one has confirmed this page's content
sources 1 — david-moreau-2026

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