Publication Bias & Research Artifacts

Abstractions about statistical distortions that arise across a body of research rather than within a single study — publication filters like the file drawer problem and funnel plot asymmetry, questionable practices like HARKing, and population-level trends like the Flynn effect.

5 abstractions in this family — domain-specific abstractions that sit near one another in structural-signature space (k-means over structural-signature embeddings). Each is shown with its short description.

• File Drawer Problem — Recognize that studies with null results disproportionately go unpublished while significant ones enter the literature, so any synthesis treating the published record as the full population of conducted research systematically overestimates effect sizes toward the filter.
• Flynn Effect — Track the sustained cohort-to-cohort rise in raw intelligence-test scores — strongest on abstract fluid-reasoning tasks and largely hidden by periodic renorming — together with its later plateau or reversal in some populations.
• Funnel Plot Asymmetry — Plot each study's effect against its precision and read a departure from the symmetric inverted-funnel expected under unbiased sampling — a gap where small null studies should be — as the visual fingerprint of a publication filter, licensing scrutiny against a fixed set of causes rather than a verdict.
• HARKing (Hypothesizing After the Results are Known) — The research practice of building a hypothesis by inspecting already-collected data and then presenting it as if it had been specified in advance, silently inflating the reported false-positive rate because the test's independence assumption is violated.
• Small-Study Effects — The meta-analytic pattern in which smaller studies report systematically larger effects than larger ones, producing funnel-plot asymmetry that inflates the pooled estimate — a shared symptom of several biases, not a diagnosis of any one cause.