Effective width is recalculated from endpoint distribution rather than nominal width

What it is

Effective target width is not a UI target's nominal geometry; it is re-estimated from the standard deviation of actual endpoint positions along the movement axis. It captures achieved precision and tolerated error under a condition, bringing user strategy, device noise, and errors into task-difficulty calculation.

Why it happens

The same nominal target yields different endpoint dispersion across devices, gain, and strategies. Effective width approximates the tolerance band people actually use: more dispersed endpoints mean wider effective width and lower effective difficulty. Used in throughput, it prevents speed bought by less accuracy from being misreported as better performance.

Studying it

Collect enough endpoint coordinates per condition, calculate standard deviation along the primary movement axis, apply the adopted standard coefficient to form effective width, then derive effective difficulty and throughput. Preserve error and outlier handling, and check for a roughly unimodal distribution; deleting large deviations artificially inflates performance.

Where it stops holding

Effective width depends on direction, endpoint definition, and distribution assumptions; it does not directly substitute visual target size or suit continuous tasks without a clear endpoint. Small samples, mixed strategies, and strongly skewed distributions make one standard deviation unstable. It is an evaluation quantity, not a sole design-size specification.

Applying it

• Preserve raw endpoint data and report effective width in device comparison, not only nominal target dimensions.
• Calculate per condition rather than pooling endpoints across directions, posture, or user groups.
• When effective width grows unexpectedly, inspect errors, tremor, gain, and target visibility instead of merely altering statistics.

Related

• Same group: C1.15.1 Throughput captures a pointing device's speed-accuracy tradeoff, not speed alone · C1.15.2 Fitts' law models movement time as a logarithmic relation to target distance and width · C1.15.4 Throughput across tasks and postures cannot be compared directly · C1.15.5 Mean completion time alone hides speed-accuracy differences
• Nearby: Q1 Research methods and evaluation · C1.03 Control-display gain
• Search terms: effective width · endpoint distribution · Fitts' law