A single-case baseline is predictable enough when its level, trend, variability, measurement quality, and sampled contexts support a defensible statement about what would likely happen next without the phase change. The decision depends on the design and clinical question. A point count or stability percentage alone cannot release a phase. Preserve the graph, decision rule, exceptions, and named reviewer.

Define what predictability means for the question

State the response, unit, direction, observation window, setting, and next-period prediction. A stable-looking count has little meaning when time or opportunity changes.

Review level, trend, and variability together

Describe the typical amount, directional movement, and scatter around the relevant level or trend. Preserve raw points beside any summary.

Check whether the sample represents ordinary conditions

List days, partners, activities, access supports, health factors, and exclusions. A narrow convenience sample limits the prediction.

Separate a design rule from clinical readiness

A baseline can be analytically interpretable while treatment, staffing, consent, assent, or safety prerequisites remain incomplete.

Record uncertainty in plain language

Name competing predictions, weak spots, and the event that would trigger another observation or a redesign.

Build Maya's baseline-predictability review

For the single-case baseline predictability decision question, create a versioned baseline-predictability review. Record the target, measurement unit, eligible opportunities or observation time, scale, desired direction, raw series, missing and invalid states, context, graph version, timing mechanism, decision rule, reviewers, clinical gates, participation response, decision time, actual change, deviations, and follow-up. The record should let another qualified reviewer reconstruct why Maya's phase decision occurred without relying on memory.

Work through Maya's baseline example

Maya has seven equal 30-minute baseline observations with counts 4, 5, 4, 6, 5, 4, and 5. The median is 5 and the range is 2. The series shows no obvious directional movement, but the team also confirms that both regular activity days and the usual communication supports were represented before calling the pattern predictable. Show the raw series and all denominators before adding summaries. This fictional after-school transition study example illustrates one decision pattern and does not create a universal stability percentage, point count, phase duration, treatment rule, or causal conclusion.

Audit the evidence available for Maya

Maya's review records seven eligible observations, identical observation windows, denominator definitions, context coverage, median 5, range 2, visual trend judgment, measurement checks, and the exact graph available at review. It states which future range the reviewer would predict and what evidence could change that prediction. The audit also checks data-entry history, definition version, observer training, agreement when needed, graph axes, session spacing, exclusions, phase labels, source timestamps, and access to original records. Unresolved discrepancies remain visible and pause any claim that depends on them.

Address the main interpretation risk for Maya

A tidy graph can still be a weak baseline when it samples one unusual setting, uses changing observation time, hides invalid sessions, or measures a response at the edge of the scale. Maya's release depends on a representative and interpretable series, rather than visual neatness alone. The review describes the observed pattern in original units, names plausible alternatives, and separates a methods judgment from a clinical recommendation. Software may calculate, graph, and surface missing evidence; qualified people interpret the series and make decisions within their authority.

Use the later phase responsibly for Maya

The later phase is compared with Maya's baseline prediction across level, trend, variability, immediacy, overlap, and replication. The report keeps clinical importance and Maya's experience separate from the narrower question of whether the baseline supported prediction. Visual analysis examines level, trend, variability, immediacy, overlap, and consistency across comparable phases or tiers. A supplemental statistic can summarize a defined feature. It cannot establish clinical importance, functional relation, consent, authorization, or treatment fit on its own.

Protect Maya's access and participation

Keep Maya's augmentative and alternative communication, interpreters, mobility, food, water, bathroom use, prescribed care, health support, rest, relationships, and emergency help available throughout the after-school transition study. Use accessible consent and assent processes when applicable and respond to withdrawal, dissent, or distress. Necessary safety or clinical action proceeds through qualified authority even when it changes the planned phase timing.

Apply current sources to Maya's decision

Maya's question uses the sources to distinguish within-phase predictability from a later functional-relation judgment. The BACB ethics hub and CASP public summary provide professional context, while the BCBA Test Content Outline identifies examination content on measurement and single-case design. The WWC Version 5.0 handbook is a research-review standard. A single-case design review describes within-phase and between-phase visual analysis. Research on waiting for baseline stability examines response-guided timing, and work on systematic visual protocols, current analytic reflections, and visual-analysis software clarifies methods and limits. ASHA supports continuous AAC access.

Rehearse Maya's review before live use

Run the baseline-predictability review with a fictional series before it governs Maya's data. Test minimum and maximum observations, missing values, equal timestamps, trend in both directions, extreme points, floor and ceiling values, a failed access gate, assent withdrawal, delayed implementation, and an amended rule. Store expected decisions, reviewer rationale, screenshots or graph versions, software version, and correction history. The rehearsal tests mechanics while leaving the live judgment to qualified reviewers.

Close Maya's phase-decision review

Review the baseline-predictability review with Maya, the responsible clinician, and a methodologist familiar with the design. Preserve raw data, graph, rule, decision snapshot, reviewers, client input, access and safety evidence, phase implementation, deviations, sensitivity checks, later outcomes, and inference limits. Keep the page draft and noindex until the manifest-named reviews are complete.

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