Abstract
Using simple, nonparametric statistical procedures can formalize the process of letting data speak for themselves, and can eliminate the gratuitous dismissal of deviant data from subjects or conditions. These procedures can act as useful discriminative stimuli, both for behavior analysts and for those from other areas of psychology who occasionally sample our journals. I also argue that changes in publication policies must change if behavior analysts are to accurately discriminate between real, reliable effects (hits) and false alarms.
Keywords: nonparametric statistics, Type I error, Type II error, conservatism
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Selected References
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- Davison M. Experimental design: Problems in understanding the dynamical behavior-environment system. Behav Anal. 1998 Fall;21(2):219–240. doi: 10.1007/BF03391965. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hopkins B. L., Cole B. L., Mason T. L. A critique of the usefulness of inferential statistics in applied behavior analysis. Behav Anal. 1998 Spring;21(1):125–137. doi: 10.1007/BF03392787. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nevin J. A. Quantitative analysis. J Exp Anal Behav. 1984 Nov;42(3):421–434. doi: 10.1901/jeab.1984.42-421. [DOI] [PMC free article] [PubMed] [Google Scholar]