They play central roles in the evaluation of equal employment opportunity and other programs, and they are used in fund allocation formulas.

Both census data types are vital ingredients for general planning, analysis, and decision making by both governmental and nongovernmental (commercial) entities of all sizes. State and local governments rely on these data for such purposes as assigning personnel to police and fire precincts, identifying the areas of a city in greatest need of service facilities, and conducting traffic planning studies. Likewise, business plans and decisions depend on census count and characteristics data: applications include locating retail outlets, comparing the market potential of different cities, and assessing the availability of needed occupational skills in different labor market areas. Both census data types are essential to many academic and private-sector researchers whose work depends on charting population differences and their changes over time.

There is no single, dominant use of census data. The significance of this fact for census evaluation is that there is no single, dominant metric against which census results can be compared in order to unequivocably determine that they are either good or bad. For example, a census could provide outstanding count data but subpar characteristics data: this could happen if serious problems occurred with the census long form. The data from such a census would be perfectly adequate for some uses but would fail to satisfy others. Similarly, the representation of the data as levels or shares, or the level of geographic aggregation, might affect one’s judgment of the quality of the data. For instance, a purely hypothetical census that—for some reason—did an excellent job of collecting information from males but not from females could still produce reasonably accurate inferences when the data are presented as shares across different geographic areas, but would suffer badly when used as count data. Similarly, changes in census processes could improve the precision of counts while hurting the use of the same data to represent changes in counts over time. For example, the change to allow multiple responses to race and ethnicity questions in the 2000 census may make it possible to capture data on more focused demographic groups, but complicate inferences about the relative sizes of minority groups relative to past censuses. A comprehensive evaluation of a census must, therefore, strive to interpret census results in the context of all of their possible uses.


At the most basic level, an ideal census evaluation would measure the differences between census-based counts or estimates and their associated true values. An estimated count greater than the true value would be considered a net overcount, and an estimated count less than the truth would be a net undercount. These differences between estimates and (unknown) truth are errors, in the statistical sense. Despite the word’s colloquial meaning, these errors are not necessarily an indication that a mistake has been made.

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