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Research

Quantitative method

A validation process designed so that results can be trusted when they are negative.

Role
Research and engineering
Period
2026
Status
Ongoing
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The problem

Analytical results are easy to make look convincing and hard to trust. Most failure modes are ways of fooling yourself: fitting noise, testing until something passes, or measuring against the wrong benchmark. The engineering problem is a process that can return a negative answer and be believed.

Approach

  • Each hypothesis is registered in writing before it is tested, so a result cannot be reinterpreted afterwards. Nine consecutive hypotheses have been recorded and falsified.

  • Validation runs out of sample in three independent ways, including a holdout of instruments never used during development.

  • An empirical null control establishes what a false positive looks like in this pipeline before any true positive is trusted.

Measured
9hypotheses pre-registered, every one falsified
3independent out-of-sample checks
1written policy governing what may run
Built with
PythonpandasNumPySQLiteAmazon EC2systemd
Stated plainly

Presented as methodology only. No performance figures are shown and none should be inferred — a research process transfers between domains, a track record does not.

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