Examining mixed unknown distributions (mud)

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.     

Manipulating power can affect memory conformity

Memory is malleable and open to influence between encoding and retrieval. Information about a past event given to us by, for example, a co‐witness can be incorporated into our own memory reports. Pairs of participants were shown 50 photographs of faces, given a power task where one had to be a designer (low‐power) and one a judge (high‐power), and then given a recognition test where one partner had to answer before the other. The individuals in the low‐power group were more influenced by their partner's responses than those in the high‐power group. This has important forensic, educational and organisational applications and shows that memory conformity can be manipulated by power. Copyright © 2007 John Wiley & Sons, Ltd.