To choose single-case randomization test statistic, match the formula to the expected effect feature and design before assignments or outcomes are known. Level, trend, immediacy, overlap, variability, and consistency statistics answer different questions. Predeclare direction, phase window, weighting, ties, missing-data handling, and aggregation across cases. Report the observed statistic as an effect summary while the randomization distribution supplies the p value.

State the expected response pattern

Describe whether change should be immediate, gradual, sustained, variable, or consistent across replications. Link that expectation to theory and the intervention schedule.

Match formula to design

A phase-level mean difference, slope difference, edge-window contrast, pairwise overlap, variance contrast, or cross-tier consistency score requires design-compatible assignments.

Fix windows and weighting

State which observations enter, how cases or tiers combine, and whether each observation, phase, or case has equal weight.

Predeclare direction and ties

Specify one- or two-sided inference, improvement direction, zero effects, tied statistics, and numerical precision before calculation.

Separate primary and exploratory analyses

Name one primary statistic for the planned test. Keep additional summaries useful and visible without treating them as interchangeable confirmatory tests.

Build Hana's a priori statistic rationale

Start with a versioned record for Hana's matching a statistic to the expected effect. For the choose single-case randomization test statistic question, record the protocol date, design, randomization unit, complete permissible assignment set, assignment probabilities, actual draw, test statistic, direction, tail, tie rule, missing-data rule, analysis mode, software, and reviewer. Keep Hana's raw time series in chronological order and link every derived value to its source. The packet should let an independent analyst reproduce both the observed statistic and its reference distribution.

Work the example for Hana

Hana's baseline is 2, 2, 3, 3 and intervention is 3, 3, 8, 8. Mean level rises from 2.5 to 5.5, a difference of 3.0. The last two baseline points and first two intervention points both average 3.0, so a two-point immediacy statistic is 0. The data illustrate a delayed level change. The protocol must choose the target feature before seeing this pattern. Preserve every intermediate assignment count, statistic, fraction, and rounding step. The example is fictional and teaches the stated method; it does not create a universal alpha, minimum design, treatment recommendation, or effect category for Hana's school meal preparation study.

Audit Hana's randomization evidence

Hana's rationale states the expected delayed change, selected mean-level statistic, one-sided direction, all-phase window, equal case weighting, missing-point rule, and alternative immediacy statistic reserved for sensitivity analysis. The record predates random assignment. The audit also checks protocol amendments, assignment deviations, duplicate or missing observations, phase-label changes, output precision, code and package versions, and whether the reported test matches the actual randomization. Unresolved discrepancies remain on hold with an owner and due date.

Prevent the main inference error in Hana's review

Selecting the largest result from several statistics inflates evidentiary claims. Hana's gate blocks metric shopping by freezing the primary statistic and reporting every preselected secondary statistic with its original role. A randomization p value depends on the assignment mechanism and predeclared statistic. It does not turn nonrandom treatment changes into randomized experiments, quantify the effect's size, establish clinical importance, or estimate a population effect.

Integrate design evidence for Hana

If the observed graph differs from the anticipated pattern, the research team reports that mismatch. It may explore another statistic descriptively, labels the analysis exploratory, and prospectively updates the next study rather than rewriting the current protocol. The WWC Version 5.0 handbook is a research-review standard rather than a universal care protocol. The responsible team still evaluates the design's prediction, verification, replication, phase patterns, attrition, assignment fidelity, measurement quality, and fit to the question.

Protect participation and access for Hana

Research design never outranks Hana's welfare. Keep augmentative and alternative communication, interpreters, mobility, food, water, bathroom use, prescribed care, health support, rest, relationships, and emergency help available. Use accessible communication for consent and assent processes, withdrawal, discomfort, and feedback. If the selected schedule becomes unwanted, unsafe, medically inappropriate, or infeasible, qualified people act for safety and document the design consequence.

Use randomized single-case sources within scope for Hana

For Hana's randomization test statistic, the BACB ethics hub and CASP public summary provide professional context, and the BCBA Test Content Outline identifies examination content on measurement and single-case design. A randomized SCED methods paper explains prospective assignment sets, randomization tests, and attainable p-value limits. An effect-measure paper describes a priori statistic choice and the boundary between tentative causal inference and effect magnitude. Research on rapidly alternating designs, changing-criterion designs, and randomized case series shows that assignment schemes and test behavior are design-specific. ASHA supports continuous AAC access.

Rehearse Hana's analysis before outcomes exist

Before the school meal-preparation study begins, Hana's team runs the a priori statistic rationale with a small fictional sequence. The dry run confirms that the assignment generator returns exactly the permitted schedules, the selected schedule can be traced to the random draw, and the randomization test statistic statistic matches a hand calculation. Reviewers deliberately include a tie, a missing observation, and an assignment at each boundary so default behavior becomes visible. They save the test inputs, expected outputs, code version, and correction log. This rehearsal checks the machinery for matching a statistic to the expected effect; it never uses Hana's future outcomes or changes the predeclared analysis after data collection.

Close Hana's methods review

Review the a priori statistic rationale with Hana, the responsible clinician, and a statistician or methodologist experienced in randomization inference. Preserve protocol, assignments, raw observations, graph, code, outputs, deviations, inference limits, client input, decisions, and future design changes. Keep the page draft and noindex until the named external reviews confirm that the research description, accessible participation, and clinical boundary are accurate.

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