RCT Bias

The fetishization of the Randomized Controlled Trial (RCT) as the "gold standard" to the point of dismissing all other forms of evidence, even in fields where RCTs are unethical, impossible, or meaningless. This bias assumes that if you can't randomize it and control it, you can't truly know it, making vast areas of social science and humanities seem illegitimate.

Example: A policymaker rejects a successful, community-developed poverty alleviation program because "there's no RCT proving it works better than a placebo intervention." The RCT bias prioritizes methodological purity over observable, real-world effectiveness, paralyzing action with impossible standards of proof.

RCT Bias by Dumu The Void February 9, 2026

RCT Bias

A cognitive and methodological bias that overvalues findings from randomized controlled trials while undervaluing evidence from other study designs (observational studies, case studies, mechanistic reasoning, qualitative research). RCT bias treats RCTs as the sole gold standard, ignoring their limitations: limited external validity, inability to study rare events, ethical constraints, and the fact that many research questions cannot be randomized. This bias leads to evidence hierarchies that dismiss useful knowledge and to policies that demand RCT evidence even when none exists or when RCTs are inappropriate. It is a form of methodological fetishism that confuses a tool with the answer.

Example: “The guideline committee rejected all observational evidence on rare side effects, insisting on RCTs—RCT bias in action, demanding impossible studies while ignoring available data.”

RCT Bias by Abzugal Nammugal Enkigal April 21, 2026

RCT Biases

The plural form, encompassing the various systematic distortions that can arise in the design, conduct, analysis, and interpretation of randomized controlled trials. These include selection bias (even after randomization), attrition bias, detection bias, performance bias, publication bias (positive results favored), sponsorship bias, and the bias of unrealistic settings (artificiality bias). RCT Biases also cover cognitive biases among researchers who overconfidently interpret p‑values, ignore baseline imbalances, or dismiss null results as “failed trials.” Recognizing RCT Biases is essential for critical appraisal; it moves beyond the myth that RCTs are inherently objective and forces attention to the many ways even well‑randomized trials can mislead.

Example: “Her training in RCT Biases taught her to check not just randomization but also who dropped out, who measured outcomes, and who funded the study—because each can bias the result.”

RCT Biases by Abzugal Nammugal Enkigal April 21, 2026