100 Percent Nonoverlap in a Small Sample: Clinical Guide

A 100 percent nonoverlap small sample result means every scored comparison met the metric's nonoverlap rule in that tiny dataset. Report phase sizes, possible score increments, raw points, pair counts, and missingness. Perfect separation can be encouraging, but it does not establish precision, persistence, functional relation, clinical importance, or generalization without adequate design, replication, and client-relevant evidence.

Report phase sizes prominently

Show baseline and intervention observation counts beside the percentage. State the number of cross-phase pairs for pairwise metrics.

Show possible score increments

With two intervention points, PND can change only in 50-point steps. This coarse resolution should shape interpretation.

Run a labeled sensitivity example

Demonstrate how one additional overlapping point would change the metric, without treating an invented point as evidence.

Review replication and duration

Ask whether the effect appears at enough phase changes, participants, behaviors, or settings for the design and question.

Pair separation with importance

Report magnitude in the original unit, client priorities, benefit, burden, access, side effects, and maintenance. Rank separation alone cannot answer those questions.

Build Wes's phase-size interpretation table

Start with a locked worklist for Wes. Record the review question, metric name, formula version, phase labels, improvement direction, planned and valid observations, missing or invalid states, tie handling, trend handling, source timestamps, calculation owner, and review date. The small-sample perfect nonoverlap result should be reproducible from this worklist without relying on an unlabeled dashboard value. Keep the data in their original unit so reviewers can connect the supplement to the graph and to the actual outcome. For the 100 percent nonoverlap small sample question, Wes's reviewer also records the release criterion, expected evidence, unresolved limitations, and exact action that the result may inform. This small decision log prevents a calculation from becoming an open-ended label and lets a later reviewer distinguish the observed value from the judgment made with it.

Work the calculation for Wes

Wes has baseline values 2 and 3 and intervention values 4 and 5, with higher values preferred. PND is 2 of 2, or 100%. NAP compares four pairs; all four improve, so NAP is 4 of 4, or 100%. One added intervention value of 3 would change PND to 2 of 3, or 66.7%. Under common NAP scoring, the added value creates one favorable pair against baseline 2 and one tie against baseline 3. The sensitivity result is therefore five favorable pairs plus one-half tie credit across six pairs: 5.5 of 6, or 91.7%. Show every intermediate count or statistic at enough precision to reproduce the displayed result. Keep the raw phase values in chronological order beside any sorted, paired, or transformed table. The fictional arithmetic illustrates the method; it does not create a clinical threshold, minimum phase size, or promised treatment effect.

Audit Wes's denominator and formula

Wes's table reports baseline n=2, intervention n=2, four observed NAP pairs, and PND increments of 50 percentage points. It labels the one-point sensitivity scenario as hypothetical, expands its NAP denominator to six pairs, and records half credit for the tied 3-versus-3 pair without adding that fictional value to the observed dataset. The audit also checks duplicate timestamps, silent imputation, phase-boundary drift, direction reversal, premature rounding, spreadsheet ranges, software version, and correction history. Any unresolved source discrepancy stays on hold with an owner and due date instead of being converted into a convenient zero or exclusion.

Watch for the main failure mode in Wes's review

A dashboard badge can hide that 100% came from only two intervention observations. Another common problem treats the same percentage as equally certain across phase sizes. Wes's report keeps the denominator in the same sentence as the result. Reviewers should be able to see how one point, tie, extreme, missing observation, or phase-length decision affects the result. A sensitivity example is labeled as hypothetical and never mixed with observed evidence.

Integrate the metric with visual and design analysis for Wes

The team reviews whether the design has enough opportunities to demonstrate an effect, whether the pattern continues, and whether the change matters to Wes. Continued measurement follows the clinical and design plan rather than a rule that perfect nonoverlap automatically ends review. The WWC Version 5.0 handbook is a research evidence standard, so its design criteria are not a universal clinical protocol. In care, the responsible clinician also considers current assessment evidence, professional scope, the treatment plan, ordinary supports, risk, and the person's choices.

Protect client relevance and access for Wes

During Wes's a 100 percent nonoverlap result from few observations review, keep augmentative and alternative communication, interpreters, mobility, food, water, bathroom use, prescribed care, health support, rest, relationships, and emergency help available. Use accessible communication to ask whether the goal, direction, magnitude, burden, and observed change matter to Wes. A favorable coefficient cannot repair an unwanted target, inaccessible measurement process, unsafe plan, or missing consent and assent process.

Use current evidence within scope for Wes

For Wes's small-sample perfect nonoverlap review, the BACB ethics hub and CASP public summary provide professional context, while the BCBA Test Content Outline supplies examination scope for measurement, graphing, interpretation, experimental design, and data-based evaluation. A single-case methods review describes common overlap, Tau, and two-SD calculations. Decision-accuracy research shows why phase size and design structure matter and why overlap percentages do not measure distance. A reproducibility tutorial documents ambiguity among Tau-U implementations and baseline corrections. Nonoverlap and mean-difference methods explain their distinct information targets. ASHA supports continuous access to AAC tools or devices.

Close Wes's supplemental analysis review

Review the phase-size interpretation table with Wes and the responsible qualified clinician. Preserve source data, graph, phase definitions, calculation specification, coverage, ties, uncertainty, limitations, client input, selected action, owner, and next review date. Reopen the analysis when the measure, phase, context, access, health, goal, software, or design changes. The final note should make clear which conclusions are supported, which remain uncertain, and which require different evidence.

How to Interpret 100 Percent Nonoverlap in a Small Sample