A new method called skewing is introduced to help decision tree algorithms handle tricky functions like parity functions more efficiently than the standard lookahead method. Lookahead struggles with larger functions due to its time complexity, but skewing maintains a constant run-time penalty. Experiments show that skewing works well with small amounts of data for functions with up to six or seven variables, even when there are many other irrelevant variables present.