January 16, 2014 Leave a comment
The simplest experimental design is the completely randomized design with 1 factor. In this design, each experimental unit is randomly assigned to each factor level. This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations). It is appealing because of its simplicity and flexibility – it can be used for a factor with any number of levels, and different treatments can have different sample sizes. After controlling for confounding variables and choosing the appropriate range and number of levels of the factor, the different treatments are applied to the different groups, and data on the resulting responses are collected. The means of the response variable in the different groups are compared; if there are significant differences, then there is evidence to suggest that the factor and the response have a causal relationship. The single-factor analysis of variance (ANOVA) model is most commonly used to analyze the data in such an experiment, but it does assume that the data in each group have a normal distribution, and that all groups have equal variance. The Kruskal-Wallis test is a non-parametric alternative to ANOVA in analyzing data from single-factor completely randomized experiments.
If the factor has 2 levels, you may think that an independent 2-sample t-test with equal variance can also be used to analyze the data. This is true, but the square of the t-test statistic in this case is just the F-test statistic in a single-factor ANOVA with 2 groups. Thus, the results of these 2 tests are the same. ANOVA generalizes the independent 2-sample t-test with equal variance to more than 2 groups.
Some textbooks state that “random assignment” means random assignment of experimental units to treatments, whereas other textbooks state that it means random assignment of treatments to experimental units. I don’t think that there is any difference between these 2 definitions, but I welcome your thoughts in the comments.