TRANSDUCTIVE SCIENCESCIENCE AFTER HUMANS / 001ED. 01 / 2026
W18 / Inference

Multiple comparisons problem

Hidden flexibility in the number of questions tested inflates apparently impressive findings.

01 / Source of departure [W18] ↗

What the reference describes.

Multiple comparisons, multiplicity or multiple testing problem occurs when many statistical tests are performed on the same dataset. Each test has its own chance of a Type I error (false positive), so the overall probability of making at least one false positive increases as the number of tests grows. In statistics, this occurs when one simultaneously considers a set of statistical inferences or estimates a subset of selected parameters based on observed values. The probability of false positives is measured through the family-wise error rate (FWER).
Shortened extract from “Multiple comparisons problem”, English Wikipedia contributors, revision 1376255469; original text licensed CC BY-SA 4.0. Excerpt shortened, line breaks normalized. For full article references and edit history, open the fixed source revision. The independent critique below is not Wikipedia text.
02 / Inherited constraint

What the process still cannot guarantee.

Hidden flexibility in the number of questions tested inflates apparently impressive findings.

That is a design criticism of how this research operation may be used, not a claim that the encyclopedia endorses the limitation. In this domain, the general redesign question is: statistical decisions become inspectable, selection-aware computations rather than one-number rituals.

03 / Machine-native reconstruction

Replace the mechanism.

Log every analysis path and apply selection-aware inference or honest independent validation.

Implementation requires a typed scientific claim, explicit operational definitions, controlled access to evidence, an immutable record of relevant transformations, and independent checking appropriate to the kind of assertion. An AI-generated interpretation without those checks would reproduce the underlying failure at greater speed.

04 / Adversarial acceptance test

What would demonstrate improvement?

Audit large search pipelines with deliberately planted null signals.

Compare with a defined baseline under matched resources, keep evaluation cases independent of the system under test, and measure both false alarms and undetected errors. Publish any negative or inconclusive result as part of the same investigation record.

Unresolved requirement. These are proposed experiments, not completed demonstrations. Better generated explanations are not proof of better scientific inference.
05 / Adjacent evidence

Continue across concepts.

Each related title has its own revision-specific Wikipedia source and a separate critical dossier. Crosslinks are exploratory; they do not imply the encyclopedia articles support the proposed replacement.

06 / Verification & provenance

No single authority.

The reference article establishes the historical and methodological subject under examination. The proposed criticism and replacement must earn their own support through reproducible tests and independent validation. Record disagreements instead of silently overwriting them.

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