For a political poll such as the one used in the example above, one would need to calibrate the weights for gender, age, income, geography, political affiliation, home ownership, occupation, and many more factors. Weighting schemes for such scenarios can be extremely complex and take dozens of different dimensions into account, depending on the sample and the analysis to be performed. ![]() For example, if the sample has a ratio of 100 males to 80 females, the weighting applied to the sample would be designed to adjust the ratio back to 49 males to 51 females as it is in the population. In traditional statistics, weighting is used to ensure that the sample being used for the analysis has the same makeup as the population that is being predicted. ![]() ![]() In data mining, we often don’t need to make adjustments to accommodate a population’s distribution factors.
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