Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies

Suzuki et al. (2023) ran computer models and checked real cases. They asked how baseline bounce changes our trust in N-of-1 trend lines.

They tested many fake data sets and 30 real graphs. Lower bounce and bigger jumps should make the trend line more right.

When the baseline is flat and calm, the model spots true effects almost every time. Wild baseline swings hide real change.

Big level or slope jumps also help the model see the shift. Small, slow changes are harder to trust.

Bigham et al. (2013) already showed stable A-B baselines give under two percent false alarms. Suzuki adds that the same calm baseline also boosts correct trend detection.

Lanovaz et al. (2019) found big effects usually replicate without a reversal phase. Suzuki explains part of why: big jumps plus low baseline noise make the change obvious.

Kok et al. (2026) pooled 270 youth externalizing cases and saw effects fade after sessions. Their raw graphs feed into the same trend rules Suzuki just sharpened.

Before you claim an intervention works for one client, look at baseline variability. If the line is jumpy, extend the phase or add data points until it calms. Then watch for a clear level or slope jump. This simple check keeps you from chasing noise and protects your clinical reputation.