The Consumer Price Index (CPI) estimation is divided into a lower level calculation of basic indexes measuring price change within item/area cells, and then an upper level aggregation of price change across cells for a target population. CPIs are currently calculated for three populations: the urban population represented by the CPI-U, the wage earner and clerical worker population represented by the CPI-W, and the elderly population represented by the CPI-E. Basic level indexes and weights serve as inputs to upper level estimation, where the weights vary by population. However, the same basic indexes are used across populations. This paper evaluates weight and price changes across populations to define differences of estimates, and then evaluates how the respective population differences impact the measure of price change. In order to measure significant differences between the CPI-U and other populations, this paper proposes a new standard error measurement methodology. The methodology uses a Jackknife approach at the area level of geographical aggregation comparable to the special (SRC) item categories published variance estimates as an alternative to the published Stratified Random Groups (SRG) method replicate based variance estimates.