10. The a priori method of claim 2 , wherein said step of preparing a set of value pairs is based on: a. obtaining frequently-occurring value pairs from said system catalog statistics; b. if said obtained frequently-occurring value pairs account for a proportion of said collected random sample of value pairs such that a statistical distribution of said collected random sample of value pairs is skewed, filtering infrequently occurring value pairs in said collected random sample of value pairs, else, bucketizing said collected random sample of value pairs by hashing into equal size subset buckets; and c. creating a number of disjoint categories equal to a number of either of: said subset buckets or said frequently-occurring value pairs; said categories computed for said chi-squared test; and d. building a contingency table for said chi-squared test; said contingency table comprising values from said selected column pairs.
10. The a priori method of claim 2 , wherein said step of preparing a set of value pairs is based on: a. obtaining frequently-occurring value pairs from said system catalog statistics; b. if said obtained frequently-occurring value pairs account for a proportion of said collected random sample of value pairs such that a statistical distribution of said collected random sample of value pairs is skewed, filtering infrequently occurring value pairs in said collected random sample of value pairs, else, bucketizing said collected random sample of value pairs by hashing into equal size subset buckets; and c. creating a number of disjoint categories equal to a number of either of: said subset buckets or said frequently-occurring value pairs; said categories computed for said chi-squared test; and d. building a contingency table for said chi-squared test; said contingency table comprising values from said selected column pairs. 13. The a priori method of claim 10 , wherein sampling error in said chi-squared test is adjusted by statistical sample variance of said random sample of value pairs.