Project summary Across many empirical fields, raw data is conditioned before it is tested: whitened, debiased, folded into composite indices. Those steps can quietly erase real structure, or manufacture apparent structure out of nothing. This project delivers an open, reusable protocol for matching the null to the statistic, and demonstrates it on public data archives. The result makes a class of measurements interpretable in either direction, which they currently are not. What are this project's goals? How will you achieve them? Goal: give fields that whiten data before testing it a correct, reusable way to separate real structure from processing artifacts. Steps: (1) write the protocol as a short decision procedure with code (for a covariance statistic the matched null is a device-permuted or time-shifted resample of the same archive, not a theoretical binomial and not a calibration run; phase-randomisation is correct only for coherence, sample-shuffling only for order); (2) re-analyse public random-event-generator archives that already record output at each conditioning stage, applying statistic-matched nulls; (3) publish, for each composite index, the floor it returns on structureless input through the identical pipeline, plus an instrument drift budget. How will you know it worked? A published, reusable protocol plus a worked re-analysis that either recovers structure the conditioning removed or sets a defensible upper bound, with all code and processed data released so others can reproduce it and apply it to their own archives. How will this funding be used? Total $12,000; minimum viable $4,500. Reference RNG and data-acquisition hardware for bench validation $1,500; compute and storage for the archive re-analysis $800; one open-access publication fee $1,700; researcher time for analysis and write-up $7,500; contingency $500. At $4,500 it ships as the protocol plus the public-archive re-analysis, with bench validation deferred. Existing lab instrumentation and additional unpaid labour are contributed. Who is on your team? What's your track record on similar projects? Jason Almasy, independent researcher running a private instrumentation and measurement lab in Dixon, New Mexico. 25 years of high-voltage and precision-instrumentation fabrication; over the past year, design and bench-validation of a real-time physiological measurement system, where this analysis originated. Presented the initial result (the floor each factor of a composite index returns on structureless input) at the ICRL Research Roundtable, November 2026. No institutional affiliation; instrumentation in hand, methods demonstrated. What are the most likely causes and outcomes if this project fails? The main risk is archive-access scope, mitigated by relying on public data that already captures each conditioning stage. Because the methods are demonstrated and the initial result exists, this is execution and write-up, not open-ended research; the likeliest shortfall is a narrower re-analysis, which still yields a published protocol and upper bounds. How much money have you raised in the last 12 months, and from where? Nothing raised to date. This is the first funding request for this work.