High precision but poor accuracy means repeated measurements are very close to each other but all are far from the true value, indicating a consistent bias or systematic error.
Imagine throwing darts that all land together in the same spot but nowhere near the bull’s-eye; that tells you something in your aiming system is off every time. In chemistry, instruments or methods can produce numbers that agree with each other yet are consistently wrong because of a systematic problem, like a miscalibrated scale or an impure reagent. Precision is about how closely repeated results match one another, while accuracy is about how close those results are to the true value. If your cluster is tight but off target, check for things that shift every result the same way rather than random noise.
Students often think precise results must be correct, but tight grouping only shows repeatability and not truth, so always test for biases; calibrate instruments or run a known standard to find offsets. Another common mistake is blaming random error when the pattern is systematic, so look for causes that would affect every measurement the same way, such as temperature, contamination, or incorrect procedure, and fix that. Some learners assume one bad reading means the whole method is flawed, but if many readings cluster tightly it actually points to a consistent issue you can correct rather than random chance.
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High Precision, Low Accuracy
- Check for miscalibrated instruments
- Test reagents for purity
- Remember precision ≠ accuracy
- Compare results to true value
- Look for consistent measurement shifts
Fixing Systematic Error in Measurements
- Assuming precision means correctness
- Blaming random error for systematic problems
- Treating one bad reading as method failure
precision, accuracy, systematicerror, calibration, measurement, dataquality, labtechnique, instrumentbias, experimentalerrors, chemistry
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