<b>On the correlation studies: most measure the wrong thing</b>

<b>On the correlation studies: most measure the wrong thing</b>

The question: what do the published 'E-E-A-T correlation' studies actually establish?

Less than their headlines claim, for a structural reason worth understanding. Studies typically take a set of ranking pages, score some observable feature — author bios present, outbound citations, page length, third-party-tool 'authority' scores — and report a correlation with position. The recurring flaw is confounding.

Sites that rank well tend to have many correlated good properties at once: better content, more links, more mentions, real authors, cleaner technical setup. Any one feature will correlate with ranking, not because it causes the ranking, but because it co-occurs with the bundle of things that do. Isolating the causal contribution of a single E-E-A-T artifact from observational SERP data is, in most published work, not actually attempted.

This is not an argument that the studies are worthless — directional correlation is information. It is an argument against the interpretive leap from 'pages with X rank better' to 'add X to rank better'. The second is a causal claim the data rarely supports, and it is the one practitioners act on.

The more credible evidence is controlled: documented before/after on a single property where one variable changed, ideally with a holdout. Those are rare and small, but they discriminate cause from co-occurrence in a way SERP-wide correlations cannot.

Caveat: even controlled single-site tests generalize poorly across niches and query classes.

What we still don't know: the causal weight of nearly any individual E-E-A-T artifact, because the clean experiments that would isolate it are mostly unrun and unpublishable at scale.