How does poor calibration affect data quality?

How does poor calibration affect data quality?

• A. It introduces bias and inaccuracy, leading to incorrect conclusions about process performance.
• B. It reduces data collection time.
• C. It has no impact on data quality.
• D. It automatically corrects measurement errors.

Answer: (A)

Calibration aligns measurements with a known standard, ensuring readings reflect the true value. When calibration is poor, the instrument introduces bias and inaccuracy, so data no longer accurately represent the actual process performance. This means conclusions about how the process is performing can be misleading, leading to incorrect decisions or actions. Data quality suffers because measurements are consistently off and less reliable for detecting real changes or trends. Calibration does not affect how long data collection takes, it does not have zero impact on data quality, and it does not automatically fix measurement errors—poor calibration actually introduces errors rather than correcting them.

Calibration aligns measurements with a known standard, ensuring readings reflect the true value. When calibration is poor, the instrument introduces bias and inaccuracy, so data no longer accurately represent the actual process performance. This means conclusions about how the process is performing can be misleading, leading to incorrect decisions or actions. Data quality suffers because measurements are consistently off and less reliable for detecting real changes or trends. Calibration does not affect how long data collection takes, it does not have zero impact on data quality, and it does not automatically fix measurement errors—poor calibration actually introduces errors rather than correcting them.