Circular analysis, or **double dipping**, is the process of exploring a dataset in an attempt to discover what relationships exist and then test hypotheses related to that exploration on the same dataset. Many statistical methods assume that hypotheses were not generated from the same dataset, so it is best to avoid circular analysis. To avoid circular analysis, one could design the analysis before observing the data. A second possibility is to subset the data into an exploratory set and a test set.