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Briefly explain loss of information in classified data. |
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Answer» In classification of data, summarizing the raw data, making it concise and comprehensible, does not show the details that are found in a raw data. There is a loss of information in classifying raw data though much is gained by summarizing it as a classified data. Once the data are grouped into classes, an individual observation has no significance in further statistical calculations. This is known as loss of information in classified data. For example, suppose class 100-200 contains 6 values viz., 120,150,160,140,180, 190. When such data is grouped as a class 100-200, then individual values have no significance and only frequency i.e., 6 is recorded and not their actual values. All values in this class are assumed to be equal to the middle value of the class-interval or class mark. Statistical calculations are based only on the values of class mark instead of the actual values. As a result, it leads to considerable loss of information. |
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