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in statistics, an important property of a sample. It consists in the closeness of characteristics of the sample—such as composition and average values—to the corresponding characteristics of the population from which the sample has been taken in accordance with established rules (seeSAMPLE SURVEY).

A judgment as to the degree of representativeness of a sample can be made in two ways. First, the sample is compared with the population with respect to all the characteristics that have been measured in both. Thus, in order to judge the representativeness of the sample households selected for a survey of household budgets, the distribution of households with respect to workers’ wage levels can be compared with the analogous distribution derived from general statistical data. If general data on the distribution are not available, average wage levels can be compared.

Second, a judgment as to the degree of representativeness can be made on the basis of the variability of the statistics under investigation in the sample. For example, if the data of a survey of household budgets indicate that the per capita consumption of bread varies from household to household much less than does the consumption of meat, then the sample can be considered to be more representative with respect to the consumption of bread than it is with respect to the consumption of meat.

The representativeness of a sample is measured by the sampling error, which is the difference between a sampling statistic and the parameter of the population from which the sample was taken. In practice, however, this difference remains unknown. Consequently, there is used as a measure of representativeness the probable value of the difference, as determined by the methods of mathematical statistics, or the root mean square of its possible values (see alsoSAMPLING).


References in periodicals archive ?
Given the importance of probability in the K-8 curriculum (National Council of Teachers of Mathematics, 2000) and the complex issues related to the representativeness heuristic and conjunction fallacy; it is important to know the extent of the problem for future K 8 teachers.
The questions for this part of the study were chosen to align with Tversky and Kahneman's classic problems relating to the representativeness heuristic and conjunction fallacy, but their style aligns more closely with the version of the classic questions used by Gerald Fast (1997) in his WDYTTCA (What Do You Think The Chances Are?
Questions one and three focus on the representativeness heuristic studied by Tversky and Kahneman (1974).
The results of this survey indicate that the representativeness heuristic can both aid and hinder participant performance.
2%) who correctly answered the birth order question and wrote an explanation, did so by using the representativeness heuristic to their advantage with comments such as "Going by my own experience and families that I know, I have seen all of these birth orders firsthand so I believe any birth order is possible.
Only one of these participants gave sound reasoning for Q3, two did not write an explanation, and the remaining participant used the representativeness heuristic to his/her advantage.
Of the 29 participants who correctly answered the coin/die problem but missed the doctor/woman problem, 24 gave written explanations, 18 of which indicated that the representativeness heuristic led the participants in the wrong direction.
These results indicate that pre-service teachers have difficulty with the representativeness heuristic, the conjunction fallacy, and deciding when situations are "equally likely.
Tversky and Kahneman (1974) suggest that people typically rely on the representativeness heuristic (RH) when answering "probabilistic questions" such as "What is the probability that event A originates from process B?
Bayes Rule as a Descriptive Model: The Representativeness Heuristic.
His explanation was: because investors use the representativeness heuristic, they would make excessively optimistic earnings forecasts for firms growing rapidly and excessively pessimistic forecasts for those in trouble, that is, high and low p/e firms, respectively.
De Bondt and I also looked for evidence of the representativeness heuristic in professional analysts' forecasts of earnings.