Random assignment versus random sampling single-case questions involve two separate mechanisms. Random assignment uses chance to select treatment orders or phase starts for the enrolled cases and can support randomization-based inference within that experiment. Random sampling uses chance to select people from a defined population and informs population representativeness. Convenience recruitment plus randomized treatment assignment does not become a representative population sample.

Draw the two selection paths

Map how people entered the study and how treatment schedules were assigned after enrollment. Store evidence for each path separately.

Name the randomization unit

State whether chance operated on phase starts, condition orders, tiers, cases, measurement occasions, or another unit.

Describe the recruitment frame

Report where potential participants came from, who was invited, eligibility, refusals, withdrawals, and contextual limits.

Scope the causal statement

Tie any randomization-based claim to the randomized design, enrolled cases, tested conditions, statistic, and assumptions.

Scope the population statement

Use replication and transparent fit evidence for transfer questions. Random assignment alone cannot estimate population prevalence or average response.

Build Ivo's inference-scope matrix

Start with a versioned record for Ivo's causal assignment and population selection. For the random assignment versus random sampling single-case 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 Ivo'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 Ivo

Ivo's study recruits eight available adults from one supported-employment program. Each person's phase start is randomly selected from a predeclared set. The assignment mechanism can support a randomization test for the enrolled cases. The convenience recruitment cannot justify estimating how all autistic adults, all workers, or all programs would respond. 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 Ivo's supported employment case series.

Audit Ivo's randomization evidence

Ivo's matrix records the recruitment frame, invitations, enrollment, exclusions, eight analyzed cases, within-case assignment sets, random draws, attrition, settings, support needs, and inference language. Separate columns identify experimental assignment evidence and population-selection evidence. 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 Ivo's review

The word randomized can obscure which step used chance. Ivo's report states randomized phase starts for eight convenience-recruited cases instead of randomized sample or representative cohort. 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 Ivo

Generalization is considered through replication across people, behaviors, settings, implementers, and time, together with transparent participant characteristics and contextual fit. The team avoids promising population effects that the sampling process did not estimate. 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 Ivo

Research design never outranks Ivo'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 Ivo

For Ivo's assignment-versus-sampling boundary, 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 Ivo's analysis before outcomes exist

Before the supported-employment case series begins, Ivo's team runs the inference-scope matrix 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 assignment-versus-sampling boundary 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 causal assignment and population selection; it never uses Ivo's future outcomes or changes the predeclared analysis after data collection.

Close Ivo's methods review

Review the inference-scope matrix with Ivo, 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.

Related resources

Sources