Precision and accuracy are distinct types of measurement error. Precision refers to random fluctuations in measurements, while accuracy (bias) reflects systematic errors. To quantify precision statistically, you collect multiple samples, verify a Gaussian distribution, compute the sample mean and standard deviation, then derive a confidence interval (CI) at a chosen confidence level (CL). The CI gives a range where the population mean is expected to reside with a specified probability. Wider CIs indicate fewer samples or higher variance, and increasing sample count narrows the interval, giving engineers a practical handle on measurement time versus precision trade-offs.
