Reluctance to use group mean as the unit of analysis is partly due to the belief that fewer observations per treatment greatly reduces the probability of detecting a treatment effect. This is discussed; equations are presented to facilitate power estimates when the group mean is the unit of analysis. (Author/BW)

Problems associated with randomization tests in single- case experiments are discussed. This article follows a discussion of randomization tests in single case studies in the same issue of this journal. (See TM 505 799; 505 801).(Author/JKS)

Two stage multiple-comparison procedures give an exact solution to problems of power and Type I errors, but require equal sample sizes in the first stage. This paper suggests a method of evaluating the experimentwise Type I error probability when the first stage has unequal sample sizes. (Author/BW)

This paper is concerned with testing hypotheses about main effects, simple effects, and interaction effects by means of analysis of variance. It presents alternative strategies for analyzing data sets for which a factorial model with two completely crossed, fixed factors is appropriate. (CTM)

The well-known problem of cumulating error probabilities is reconsidered from a general epistemological perspective, namely, the concepts of severity and of fairness of tests. It is shown that not only Type 1 but also Type 2 errors can cumulate. A new adjustment strategy is proposed and applied. (Author/JAZ)

It is shown that using formulae for the estimation of sampling errors based on simple random sampling, when a design actually involves cluster sampling, can lead to serious underestimation of error. Jackknife and balanced repeated replication are recommended as techniques for dealing with this problem. (Author/CTM)