Among high-ability groups, outcomes may hinge more on personality and mental health than intelligence, but IQ looks dominant because it’s measured cleanly while personality is noisy. Measurement error attenuates correlations, steering research and policy toward what’s convenient to quantify rather than what matters most.
— It warns that evidence hierarchies and selection systems can misallocate attention and resources by overvaluing the most measurable traits.
@degenrolf
2025.10.17
54% relevant
Star ratings are an 'easy measure' of quality; this finding shows they are confounded by day‑of‑week mood/behavior, illustrating how convenient metrics encode bias and can mislead decisions if not adjusted.
Tyler Cowen
2025.10.15
72% relevant
Both argue that what we see depends heavily on the measurement scheme: here, normalizing across eras and defining 'greatness' relative to predecessors (yielding ~1/n chances for new maxima) can create an apparent decline, just as adding controls or the wrong sampling frame can manufacture misleading causal results.
Noah Smith
2025.09.29
65% relevant
The article argues economists preferred mathematically tractable, simple market models, which privileged what could be cleanly formalized over messier realities—echoing the broader point that convenience of method can steer conclusions and policy.
Tyler Cowen
2025.09.04
60% relevant
The JPE study argues prevailing intelligence and 'competitiveness' measures omit confidence and incentive alignment, potentially biasing conclusions; this echoes the broader point that what’s easiest to measure can distort inference about what matters.
Tyler Cowen
2025.09.01
68% relevant
The paper shows compensation tracks legible metrics—journal tier placements—while teaching and other harder‑to‑measure outputs matter far less, exemplifying how what’s easy to quantify ends up driving resource allocation and status.
Matt Bruenig
2025.08.21
72% relevant
Bruenig’s jab at studying whether cash changes a child’s BMI at age 4 mirrors the critique that policy chases what’s easy to measure, missing harder-to-quantify outcomes like social belonging and class alienation.
Davide Piffer
2025.08.20
70% relevant
Relying on readily available nutrition supply data leads to the 'dairy makes Dutch tall' claim; incorporating harder-to-measure genetic PGS reshapes the inference, illustrating how convenient metrics can dominate explanations unless key variables are added.
Sebastian Jensen
2025.08.19
70% relevant
The article argues that convenient genome‑wide relatedness measures (GREML/GCTA, RDR, sib‑regression) undercount heritability because they assume additivity, no assortative mating, and proxy trait‑causal loci with whole‑genome similarity, paralleling the idea that what’s easy to quantify can bias inference.
Steve Stewart-Williams
2025.08.16
70% relevant
The paper shows the convenient metric ('body count' as a simple tally) is less informative than harder‑to‑measure dynamics (recency and whether partner accrual is slowing), mirroring how easy but noisy measures can mislead conclusions about what really matters.
Arnold Kling
2025.08.15
100% relevant
Emil Kirkegaard’s claim about personality vs. g, paired with Arnold Kling’s emphasis on measurement error.
Sebastian Jensen
2025.08.08
60% relevant
The post explicitly addresses measurement reliability (fluid vs crystallized tests) and shows how test design can bias g-loadings; the Project Talent comparison equalizes reliability to avoid attenuation, echoing the broader warning that what’s easiest to measure can distort inference.