Four trap tests behind the 20,000 leads

1. Saturation destroys a comparison even when it does not flip a ranking

Two materials with different sorption behaviour were compared at concentrations from 10⁻² to 10²:

The ratio changes by a factor of 13.4 with no change in the materials. The ranking does not flip — which is precisely the trap: a stable ranking is not evidence that the measurement is unsaturated. A ranking can be numerically meaningless while remaining ordinally correct.

Practical use. Any supplier comparison of sorbents, catalysts or membranes should report the concentration at which it was made, and repeat at a second concentration. A ratio that moves by more than a factor of two disqualifies the comparison as a material property.

2. A derived variable inflates apparent explanatory power by 3.6×

With a true relation y = 0.5x + noise, the direct R² is 0.211. Forming z = x + y — a derived variable that contains the outcome — raises R² against y to 0.754, an inflation of 3.57×.

Practical use. Before quoting a correlation in a catalogue-driven field, check whether the predictor is computed from the outcome. This is the same failure documented in the fireball analysis, where impact energy was a recalibration of radiated energy and correlated at 0.99992.

3. Cutting the bottom 10% biases the mean upward by 9%

For a lognormal population (20,000 draws), removing values below the 10th percentile — the ordinary detection limit — raises the mean from 1.376 to 1.501, a +9.0% bias.

Practical use. Any mean quoted from a detection-limited instrument is high by roughly the missing fraction times the shape factor. The correction is not a constant: it depends on the distribution, so the honest move is to report the censoring fraction alongside the mean.

4. An uncorrected blank drift produces a 497% error

A constant true signal of 1.0 with a linear instrumental drift over 200 samples gives a measured mean of 5.975 — an error of 497%. Averaging does not help: the drift is systematic, so more samples move the mean further along the drift.

Practical use. A blank measured once at the start is not a blank. Measuring it at both ends of a run converts an unbounded systematic into a bounded one, and the difference between the two blanks is the uncertainty on every sample in between.

5. What these four tests establish about the catalogue

Each trap occupies exactly 1,000 of the 20,000 leads, because the composition is balanced. Four numbers therefore speak to four thousand leads — not to all twenty thousand, and the other 16,000 traps remain untested.

6. Strongest results, and what to test next

1. Derived-variable inflation (3.57×). Cheapest to act on: a check for whether one reported quantity is computed from another. Next test: apply it to a real catalogue and count how many pairs are derived.
2. Blank drift (497%). Largest effect. Next test: closing blanks on an instrument in routine use, reported as a time series.
3. Saturation (13.4×). Most common in practice. Next test: repeat a published comparison at a second concentration.
4. Truncation (+9%). Smallest but most systematic. Next test: report censoring fractions in a table of means.

7. Limits

• The parameters are illustrative. 13.4×, 3.57×, 9.0% and 497% are properties of the chosen numbers, not universal constants. The direction of each effect is robust; the size is not.
• Simulation, not measurement. Nothing here observes a real instrument.
• Four of twenty traps. The catalogue's other 16,000 leads rest on the same structural argument, untested.

If one sentence survives: four ordinary measurement traps produce errors of 9% to 497% in the simplest possible simulation, and each of them is invisible in the number that gets reported.

Method and composition · Top 50 leads