Which of the following is the most appropriate criticism of excessive classification of data ?
It may obscure the overall pattern by creating too many small groups
Classification arranges raw data into classes so that their essential features stand out. Standard texts (S.P. Gupta, Statistical Methods) stress that the number of classes should be neither too small nor too large; rules such as Sturges’ formula \(k = 1 + 3.322 \log_{10} N\) are used to keep it manageable.
The very purpose of classification is to condense data and bring out the general pattern. When the data are split into too many small groups, each class holds only a few items, frequencies fluctuate irregularly and the overall shape of the distribution is obscured rather than revealed. That is the standard criticism of excessive classification.
Excessive classification does not always increase accuracy; beyond a point it only adds noise. It does not convert quantitative data into qualitative data. The claim that it makes statistical analysis impossible in every case is an absolute overstatement: an over-fine table can be analysed, for example by regrouping classes or by working with the ungrouped values, so the defect is loss of clarity, not impossibility.
It may obscure the overall pattern by creating too many small groups option is correct.
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