Rigorous Experiments Agent Skill

Rigorous Experiments

Run statistical experiments on observational/personal time-series data that

survive scrutiny. Distilled from a 54-experiment n-of-1 program in which

sampled permutation tests, missing-data artifacts, app-categorization bugs

and collinear mechanisms repeatedly manufactured — and then destroyed —

"findings". Every rule here exists because its absence once produced a

wrong conclusion.

Modes

Pick the mode matching the request; chain them for a full study.

| Mode | When | Reference |

|------|------|-----------|

| design | New hypothesis or study | references/design.md |

| conduct | Implementing + running the experiment | references/statistics.md |

| validate-data | Before trusting ANY new data source | references/data-validation.md |

| cross-validate | Findings worth defending; code review; external model review (e.g. GPT Pro) | references/cross-validation.md |

| investigate-leads | A sweep/run produced leads (p<0.06, not FDR-confirmed) | references/lead-investigation.md |

| audit | Re-examining past claims, registries of findings | references/statistics.md §Audit |

Non-negotiable core (all modes)

• Pre-register before computing. Hypotheses, exact tests, family size

m, and the acceptance threshold go in the script docstring BEFORE the

first run. Post-hoc tests are reported as descriptive, never promoted.

• Exact permutation, never sampled, on small n. A session sequence of

n=19 has 18 circular shifts: the minimum honest p is ~1/19≈0.05.

Sampling 2000 shifts with replacement fabricates precision (this killed

a flagship "q=0.028" finding). Use scripts/perm_stats.py.

• Permute over the full calendar, not the compressed series. Shifting

a gap-compressed series breaks the timeline; keep missingness as NaN

masks re-applied per shift. Event indicators must be pure 0/1 with no

gaps — missingness lives only in the outcome series.

• BH with FIXED family size m, a LITERAL CONSTANT declared at design

time — never len(tests) (that defeats pre-registration; the linter

rejects it). Assert the run matches the declared m. Confirmatory

families small and separate from exploratory sweeps; pooling everything

into one BH buries true effects, cherry-picking families manufactures

them. Plain BH assumes independent/positively-dependent tests; for

strongly dependent lag families use BH-Yekutieli or maxT resampling.

• Stationarity check before correlating trending series. Exact

circular shift on a trending series is "exactly, reproducibly wrong":

report prewhitened-r (AR1 residuals) and stationary bootstrap alongside.

• Stratify before pooling (Simpson check): within group (e.g.

therapy/coaching) and within regime (pre/post known breaks). A pooled

r=−0.25 once hid therapy −0.64 vs coaching +0.53.

• Controls can re-describe a finding, not just kill it. When a control

collapses an effect, check collinearity of control and predictor —

r(self-focus, session-length)=0.79 meant "mechanism ambiguous", not

"effect fake". Report the decomposition.

• Honest statuses: confirmed (q<0.10 exact) ≠ lead (p<0.06) ≠ null ≠

descriptive. Status flips are recorded, never silently edited. Nulls

with adequate power are findings. Robust ≠ significant: a lead

surviving leave-one-out at small n is still underpowered — a candidate

for prospective test, not a finding.

8b. Series scope is part of the test. A lagged "[t+1]" means the next

unit in the series the hypothesis is about, not the next pooled row;

define scope before lagging (it once flipped a sign). When recomputing

a prior result, reproduce a stored artifact on that scope first.

• Privacy: raw text/audio never enters output files or external

uploads — statistics, rates and embedding-derived scores only.

10. Plain-language reporting: every statistic carries its practical

meaning inline; define r/p/q/n once per report; no untranslated jargon

calques. Narrative first, numbers as support.

Workflow (full study)

• validate-data gate on any new source (see reference — the checklist

has caught: zero-vs-missing conflation, dedup semantics, substring

category bugs, rolling purge windows, timezone conventions).

• design: pre-registered hypotheses + family + power sanity.
• conduct: implement with scripts/perm_stats.py; run; write results

JSON with tests, statuses, and caveats including known limitations.

• cross-validate: adversarial code review (e.g. Codex read-only) BEFORE

trusting results; fix findings; re-run. For major claims, external

model review with a privacy-screened archive.

• investigate-leads on anything that surfaced as a lead (not at the

same scale — the triage battery: LOO, directionality, detrend-vs-step,

within-cycle, prewhiten+bootstrap; consolidate same-direction leads

into one composite). Mark diagnostic runs descriptive_only: true.

• Verdicts in honest prose (mixed/rejected allowed); report; registry

update with status provenance.

Viewing results

Launch the bundled explorer over any directory of results JSONs:

python3 scripts/explorer.py <results_dir> [--port 8799] [--pattern "exp*.json"] [--sort newest|oldest]

Generates explorer.html in the directory, starts (or reuses) a loopback

http server on the port, and opens the browser: experiment list with

confirmed/lead badges, filter, sortable test tables color-coded by

status, verdicts, caveats, raw JSON. The page fetches result files live —

re-running experiments updates the view; re-run the script only when new

result files appear. Serve over localhost, never file:// (CDN fonts) and

never on a non-loopback interface (results may contain personal

statistics).

Evals

Run python3 evals/run_evals.py (from the skill directory) to lint an

experiment script/results pair against the standards (pre-registration

present, fixed literal m, exact perm usage, caveats, no raw text in

outputs). A diagnostic/triage run that intentionally mints no new tests

sets descriptive_only: true in its results JSON to satisfy the

"has tests" check. Eval cases in evals/cases/ document expected

pass/fail examples.