Loss of Information in Classification

The inherent shortcoming of classifying data as a frequency distribution — where the details of raw data are lost — is called:

Correct Answer Explanation

The inherent shortcoming of a frequency distribution where details of raw data are not available is called Loss of Information.

Key Points:

• While classification summarises raw data making it concise and comprehensible, it does not show individual observation details.
• Once data is grouped, only the class frequency is available, not the actual values within the class.
• Example: Class 20–30 shows frequency = 6, but we cannot tell that the actual values were 25, 25, 20, 22, 25, 28.
• Further statistical calculations are based on class mark (25), not individual values — this causes loss of precision.