Editing Fitts's law (section)

== Characterizing performance ==
Since the ''a'' and ''b'' parameters should capture movement times over a potentially wide range of task geometries, they can serve as a performance metric for a given interface. In doing so, it is necessary to separate variation between users from variation between interfaces. 
The ''a'' parameter is typically positive and close to zero, and sometimes ignored in characterizing average performance, as in Fitts' original experiment.<ref name=Soukoreff2011/> Multiple methods exist for identifying parameters from experimental data, and the choice of method is the subject of heated debate, since method variation can result in parameter differences that overwhelm underlying performance differences.<ref name=Soukoreff2004>{{cite journal |title=Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI |first1=R. William |last1=Soukoreff |first2=I. Scott |last2=MacKenzie |journal=International Journal of Human-Computer Studies |year=2004 |volume=61 |issue=6 |pages=751–789 |url=https://web.archive.org/web/20170808194940id_/https://soukoreff.com/academic/ijhcs2004.pdf |doi=10.1016/j.ijhcs.2004.09.001 |s2cid=12224119 }}</ref><ref name=Zhai2002>{{cite tech report |last=Zhai |first=Shumin |date=2002 |title=On the Validity of Throughput as a Characteristic of Computer Input |number=RJ 10253 |institution=Almaden Research Center |location=San Jose, California |url=http://www.m.shuminzhai.com/papers/ZhaiIBMReporRJ10253.pdf |format=pdf}}</ref>

An additional issue in characterizing performance is incorporating success rate: an aggressive user can achieve shorter movement times at the cost of experimental trials in which the target is missed. If the latter are not incorporated into the model, then average movement times can be artificially decreased.