How do you turn a marketing claim into a testable question?

This dossier turns the claim into a contract containing a population, unit, exposure, comparator, outcome, horizon, minimum useful effect, analysis plan, and falsifier. Its output validates contract completeness, not a marketing effect.

1. Decision-maker: verify the decision and useful threshold.
2. Practitioner: fix population, unit, outcome, and horizon.
3. Analyst: seal estimand, analysis, uncertainty, and falsifier.

A team wants to decide whether an email campaign deserves a test. The population is limited to eligible subscribers, the unit is the subscriber, the comparator is no email, and the outcome is a purchase within seven days.

Among eligible subscribers, does assignment to email rather than no email increase purchase within seven days by at least 1.0 percentage point? The planned estimand is an intention-to-treat risk difference.

Without a unit, dependence among observations is unknown. Without a comparator, the effect is undefined. Without a horizon, the outcome can move after observation. Without a threshold and falsifier, almost any result can be presented as favorable.

A testable question fixes in advance what will be observed, for whom, against what, when, and under which rule the result may support or contradict the hypothesis. It thereby separates idea generation from confirmatory testing.

The register requires population and eligibility, assignment and analysis units, intervention, comparator, outcome definition and window, estimand, intercurrent-event strategy, missing-outcome rule, estimator, interval method, alpha, multiplicity, threshold, direction, three decision outcomes, protocol deviations, and data status.

Q=(P,U,X,C,Y,H,Δ*,D,A,F), where P is population, U unit, X exposure, C comparator, Y outcome, H horizon, Δ* minimum useful effect, D direction, A analysis plan, and F falsifier.

Python and R read the supplied path, require one row, validate fields, H>0, Δ*>0, 95% ↔ alpha 0.05 consistency, the direction-bound pair, ITT denominator, intercurrent events, fail-closed missing-outcome rule, multiplicity, and deviations, then compute the same canonical SHA-256.

The protocol fixes H=7 days, Δ*=1.0 point, and one contrast. The Miettinen-Nurminen CI inverts the constrained binomial score, applies N/(N-1), tolerance 1e-8, 100 iterations, 95% level, and two-sided alpha 0.05. Lower bound ≥ +1.0: threshold supported; upper bound < +1.0: falsified; otherwise inconclusive. protocol_complete=true; SHA-256=86a315abd306359f92ec5439f361511d80a7516af3516525c8bd1272014caa74; no effect estimated.

Synthetic protocol example—no observations and no estimated effect.

Fields must denote observable, nonredundant objects. The unit must match the assignment mechanism. The outcome must be measurable for all groups over the same horizon. The useful threshold must come from the decision, not future results.

Check population, unique assignments, complete transaction-log coverage, delivery, opening, crossover, outside exposure, timing, exclusions, multiplicity, and deviations. Anticipated precision can be planned from sample size and assumed rates; the empirical interval can only be calculated after observation.

protocol_complete=true means only that the register contains the required fields and passes deterministic rules. It proves neither design feasibility, valid assignment, nor that email will produce the minimum useful effect.

Supported: the question, planned estimand, threshold, and falsifier are explicit. Forbidden: email increases purchases, the protocol guarantees an outcome, or merely stating a threshold makes the future test causal.

The team may accept, revise, or reject the protocol before collection, estimate test cost, verify variable availability, and decide whether the 1.0-point threshold justifies the experiment. It cannot yet act on a nonexistent observed effect.

Use before collection or before outcomes are opened. Stop if population, comparator, outcome, or horizon cannot be fixed, if the threshold has no decision rationale, or if the plan is rewritten after results are seen without an exploratory label.

Deliver the canonical question, versioned and hashed register, rationale for Δ*, analysis plan, missing-data rule, falsifier, protocol deviations, and confirmatory or exploratory label for every analysis.