True or False: Representing missing values with a sentinel value like -99 treats the sentinel as a real value rather than as missing.

True or False: Representing missing values with a sentinel value like -99 treats the sentinel as a real value rather than as missing.

• A. True
• B. False
• C. Not specified
• D. Not applicable

Answer: (A)

Sentinel values are placeholders for missing data, but they are not inherently missing. When a dataset uses a value like -99 to stand in for missing, software will treat -99 as a real number unless there is a rule to interpret it as missing. As a result, that value will participate in calculations and summaries, which can bias results. To ensure missing data is handled correctly, you should remap these sentinels to a recognized missing marker (such as NULL/NaN) or configure the system to treat the sentinel as missing. Therefore, representing missing values with a sentinel value like -99 leads to the sentinel being treated as a real value rather than as missing.

Sentinel values are placeholders for missing data, but they are not inherently missing. When a dataset uses a value like -99 to stand in for missing, software will treat -99 as a real number unless there is a rule to interpret it as missing. As a result, that value will participate in calculations and summaries, which can bias results. To ensure missing data is handled correctly, you should remap these sentinels to a recognized missing marker (such as NULL/NaN) or configure the system to treat the sentinel as missing. Therefore, representing missing values with a sentinel value like -99 leads to the sentinel being treated as a real value rather than as missing.