title: Examining mixed unknown distributions (mud).
abstract: A function, written in R, for testing whether the distribution of responses in one condition can be considered a combination of the distributions from two other conditions is described. The important aspect of this function is that it does not make any assumptions about the shape of the distributions. It is based o nthe Kolmogorov-Smirnov D statistic. The function also allows the user to test more specific and, hence, more statistically powerful hypotheses. One hypothesis, that the mixture does not capture the middle third of the distribution, is included as a built-in option, and code is provided so that other alternatives can easily be run. A power analysis reveals that the function is most likely to detect a difference between the combined conditions' distribution and the other distribution when the center of the other distribution is near the midpoint of the two original distributions. Critical p values are estimated for each set of distributions, using bootstrap methods. An example from human memory research, exploring the blending hypothesis of the misinformation effect, is used for illustrative purposes.
authors.affiliation: Department of Psychology, University of Sussex, Brighton, England. danw@sussex.ac.uk
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Wright
authors.forename: Elin M
authors.initials: EM
authors.lastname: Skagerberg
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D008568:Memory / Q000502:physiology; D011939:Mental Recall / Q000502:physiology; D012984:Software