Precision and accuracy are two fundamental concepts in the Measure Phase of Lean Six Sigma that are essential for understanding measurement system quality. While often confused, these terms have distinct meanings that practitioners must understand to ensure reliable data collection.
Accuracy refer…Precision and accuracy are two fundamental concepts in the Measure Phase of Lean Six Sigma that are essential for understanding measurement system quality. While often confused, these terms have distinct meanings that practitioners must understand to ensure reliable data collection.
Accuracy refers to how close a measured value is to the true or actual value. Think of it as hitting the bullseye on a target. When a measurement system is accurate, the average of multiple measurements will be very close to the known reference value. Accuracy issues typically arise from calibration problems, worn equipment, or environmental factors affecting the measurement device. To assess accuracy, you compare your measurements against a known standard or reference value.
Precision, on the other hand, describes the consistency or repeatability of measurements. It measures how close multiple measurements are to each other, regardless of whether they are close to the true value. Using the target analogy, precision means all your shots are grouped tightly together, even if that group is far from the center. Precision encompasses both repeatability (same operator, same conditions) and reproducibility (different operators or conditions).
Understanding the relationship between these concepts is crucial. A measurement system can be precise but not accurate (consistent measurements that are all off-target), accurate but not precise (measurements averaging to the correct value but with high variation), neither precise nor accurate, or both precise and accurate (the ideal state).
During the Measure Phase, practitioners use Measurement System Analysis (MSA) tools like Gage R&R studies to evaluate both precision and accuracy. Bias studies assess accuracy by comparing measurements to reference standards, while repeatability and reproducibility studies evaluate precision components.
Ensuring both precision and accuracy in your measurement system is critical because flawed measurements lead to incorrect conclusions, potentially causing teams to solve the wrong problems or miss significant improvement opportunities.
Precision and Accuracy in Six Sigma Green Belt - Measure Phase
Introduction
Precision and accuracy are fundamental concepts in the Measure Phase of Six Sigma methodology. Understanding these concepts is critical for collecting reliable data and making sound decisions about process improvement.
Why Is This Important?
In Six Sigma projects, the quality of your conclusions depends entirely on the quality of your measurements. If your measurement system lacks precision or accuracy, you cannot trust your data, which means you cannot:
• Properly identify the true baseline performance • Detect meaningful changes in process performance • Make valid comparisons between before and after improvements • Correctly identify root causes of variation
What Is Accuracy?
Accuracy refers to how close a measured value is to the true or actual value. A measurement system is accurate when the average of repeated measurements equals the true value. Accuracy is also known as bias in measurement system analysis.
Example: If a scale consistently shows 10.0 kg when measuring a standard weight of 10.0 kg, it is accurate.
What Is Precision?
Precision refers to the repeatability and consistency of measurements. A precise measurement system produces similar results when the same item is measured multiple times. Precision relates to the spread or variation in measurements.
Example: If you weigh the same object five times and get 9.8, 9.9, 9.8, 9.9, 9.8 kg, the measurements are precise (low variation) even if not accurate.
How They Work Together
There are four possible combinations:
1. High Accuracy, High Precision: Measurements cluster tightly around the true value - this is the ideal state
2. High Accuracy, Low Precision: Measurements average to the true value but are widely scattered
3. Low Accuracy, High Precision: Measurements cluster tightly but are consistently off from the true value (systematic bias)
4. Low Accuracy, Low Precision: Measurements are scattered and not centered on the true value - worst case
The Target Analogy
Think of a dartboard or archery target:
• Accurate: Darts hit near the bullseye center • Precise: Darts are grouped closely together • Accurate AND Precise: Darts are grouped tightly around the bullseye
Measurement System Analysis (MSA) Connection
In the Measure Phase, Gage R&R studies assess precision through:
• Repeatability: Variation when the same operator measures the same part multiple times • Reproducibility: Variation when different operators measure the same part
Accuracy is assessed through calibration studies comparing measurements to known standards.
Exam Tips: Answering Questions on Precision and Accuracy
Tip 1: Remember the key distinction - Accuracy = closeness to TRUE value, Precision = closeness to EACH OTHER
Tip 2: When shown a target diagram, look for clustering (precision) and proximity to center (accuracy) as separate assessments
Tip 3: Know that precision problems create random error while accuracy problems create systematic error (bias)
Tip 4: If a question asks about calibration, it relates to accuracy. If it asks about repeatability or reproducibility, it relates to precision
Tip 5: A measurement system can be precise but inaccurate - this is correctable through calibration
Tip 6: Remember that improving accuracy requires identifying and removing bias, while improving precision requires reducing variation in the measurement process
Tip 7: Questions may use alternative terms - bias for accuracy issues, and variation or spread for precision issues
Tip 8: In scenario questions, identify whether the problem is consistency of measurements (precision) or correctness of measurements (accuracy) before selecting your answer
Common Exam Question Formats:
• Identifying accuracy vs precision from target diagrams • Determining which type of error exists from measurement data • Selecting the appropriate corrective action for each type of problem • Connecting MSA concepts to precision and accuracy