Randomization results alongside visual analysis should answer complementary questions. A valid p value summarizes how extreme the predeclared statistic is under the actual assignment scheme. Visual analysis describes level, trend, variability, immediacy, overlap, and consistency across replications. Add original-unit magnitude, client priorities, burden, side effects, maintenance, generalization, assignment deviations, and uncertainty. Neither result should erase a conflicting or heterogeneous pattern.

Report the randomization result completely

Give design, assignment mechanism, statistic, tail, exact or sampled method, numerator, denominator or draws, p value, deviations, and software details.

Report the six visual features

Describe level, trend, variability, immediacy, overlap, and consistency in similar phases for each case, tier, behavior, or setting.

Add magnitude and lived relevance

Use original units, client-selected goals, experience, access, benefit, burden, side effects, maintenance, and generalization.

Explain convergence and divergence

State where the planned test and visual review agree, where they differ, and which design or data features may explain the difference.

Keep clinical and research decisions separate

Assign qualified owners for care decisions, research interpretation, data management, and safety. A p value does not authorize treatment or override withdrawal.

Build Lian's multi-evidence result table

Start with a versioned record for Lian's statistical and visual single-case interpretation. For the randomization results alongside visual analysis 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 Lian'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 Lian

Lian's three-tier randomized multiple-baseline study yields an exact one-sided p value of 0.04 for the predeclared across-tier level statistic. The example ledger does not provide the at-least-as-extreme count or total permissible assignments, so 0.04 cannot be independently recomputed from this page alone. The release report must give that exact fraction. The first tier changes immediately, the second changes after a delay, and the third remains variable. The report gives p=0.04 while describing that inconsistency and reporting each tier's raw values and client feedback. 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 Lian's community transit training study.

Audit Lian's randomization evidence

Lian's table links the protocol, assignment set, exact numerator and denominator, statistic, graph, level, trend, variability, immediacy, overlap, similar-phase consistency, original-unit change, missingness, deviations, assent record, access supports, and tier-specific interpretation. No cell converts p=0.04 into an effect-size label. 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 Lian's review

A report can use statistical significance to silence visual disagreement, or use a favorable graph to omit an inconclusive planned test. Lian's template requires both planned analyses and an explanation of convergence or divergence. 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 Lian

The responsible team decides what additional evidence is needed for the research question and what action is appropriate for each person. Clinical care follows current assessment, welfare, consent and assent, and professional judgment; research significance never requires continuing an unwanted or unsafe condition. 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 Lian

Research design never outranks Lian'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 Lian

For Lian's integrated randomization and visual report, 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 Lian's analysis before outcomes exist

Before the community transit-training study begins, Lian's team runs the multi-evidence result table 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 integrated randomization and visual report 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 statistical and visual single-case interpretation; it never uses Lian's future outcomes or changes the predeclared analysis after data collection.

Close Lian's methods review

Review the multi-evidence result table with Lian, 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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