Background: Do we always need complex methods for software effort estimation (SEE)? Aim: To characterize the essential content of SEE data; i.e. the least number of features and instances required to capture the information within SEE data. If the essential content is very small then (1) the contained information must be very brief and (2) the value-added of complex learning schemes must be minimal. Method: Our QUICK method computes the Euclidean distance between rows (instances) and columns (features) of SEE data; then prunes synonyms (similar features) and outliers (distant instances); then assesses the reduced data by comparing predictions from (1) a simple learner using the reduced data and (2) a state-of-the-art learner (CART) using all data. Performance is measured using hold-out experiments and expressed in terms of mean and median MRE, MAR, PRED(25), MBRE, MIBRE, or MMER. Results: For 18 data sets, QUICK pruned 69% to 96% of the training data (median=89%). K=1 nearest neighbor predictions (in the reduced data) performed as well as CART’s predictions (using all data). Conclusion: The essential content of some SEE data sets is very small. Complex estimation methods may be over-elaborate for such data sets and can be simplified. We offer QUICK as an example of such a simpler SEE method.