Discriminant function analysis is very similar to [[logistic regression]], and both can be used to answer the same research questions.<ref name="green"/> Logistic regression does not have as many assumptions and restrictions as discriminant analysis. However, when discriminant analysis’ assumptions are met, it is more powerful than logistic regression.<ref>{{citationcite neededbook|dateauthor1=AprilTrevor 2012Hastie|author2=Robert Tibshirani|author3=Jerome Friedman|title=The Elements of Statistical Learning. Data Mining, Inference, and Prediction|edition=second|publisher=Springer|page=128.}}</ref> Unlike logistic regression, discriminant analysis can be used with small sample sizes. It has been shown that when sample sizes are equal, and homogeneity of variance/covariance holds, discriminant analysis is more accurate.<ref name="buy"/> With all this being considered, logistic regression has become the common choice, since the assumptions of discriminant analysis are rarely met.<ref name="cohen"/><ref name="buy"/>
