Data organized into discrete categories or *classes* may present problems for certain analyses if the number of observations ($n$) belonging to each class is not constant across classes. Classes with unequal $n$ are *unbalanced*.
Data organized into discrete categories or classes may present problems for certain analyses if the number of observations ($n$) belonging to each class is not constant across classes. Classes with unequal $n$ are unbalanced. This tag should be used for questions about datasets with subsamples of unequal size where imbalanced distributions across categorical factors is of concern.
Analyses with known, non-negligible sensitivity to unbalanced classes include (but are not limited to):
- Student's $t$-test
- Analysis of variance (see also Howell, 2009)
- Some $\chi^2$ tests
- Tukey's range test and the Newman–Keuls method
- Analysis of differential item functioning using item response theory
Reference
Howell, D. C. (2009). Unequal sample sizes do matter. University of Vermont. Retrieved from http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Unequal-ns/unequal-ns.html.
