In data mining, missing value means zero.

In data mining, missing value means zero.

• A. False
• B. True
• C. Not necessarily
• D. Cannot be determined

Answer: (A)

Missing values indicate unknown information, not the value zero. Zero is a real, valid data value for many features, while missing means we simply don’t know what the value should be. Replacing missing values with zero can seriously distort the data—for example, turning a missing age into 0 would imply a newborn, which is almost certainly incorrect and would lower the average age. Similarly, imputing zero for missing income would falsely suggest zero earnings. In practice, missing data are handled with appropriate methods (deletion, imputation based on other data, or models that accommodate missingness) and the reason data are missing (why it’s missing) guides the approach. So the statement is not correct.

Missing values indicate unknown information, not the value zero. Zero is a real, valid data value for many features, while missing means we simply don’t know what the value should be. Replacing missing values with zero can seriously distort the data—for example, turning a missing age into 0 would imply a newborn, which is almost certainly incorrect and would lower the average age. Similarly, imputing zero for missing income would falsely suggest zero earnings. In practice, missing data are handled with appropriate methods (deletion, imputation based on other data, or models that accommodate missingness) and the reason data are missing (why it’s missing) guides the approach. So the statement is not correct.