Type A Uncertainty is measurement uncertainty evaluated by statistical analysis of a series of observations — determined from the standard deviation of repeated measurements, Type A uncertainty is calculated from actual measurement data using established statistical methods.
Type A Evaluation
- Method: Make $n$ repeated measurements of the same quantity — calculate the sample standard deviation $s$.
- Standard Uncertainty: $u_A = s / sqrt{n}$ — the standard deviation of the mean.
- Degrees of Freedom: $
u = n - 1$ — more measurements give more reliable uncertainty estimates.
- Distribution: Usually assumed normal — Student's t-distribution for small sample sizes.
Why It Matters
- Data-Driven: Type A uncertainty comes directly from measurements — the most defensible uncertainty estimate.
- Repeatability: The Type A uncertainty from repeated measurements captures the measurement repeatability.
- Combined: Type A uncertainties are combined with Type B uncertainties using RSS (root sum of squares).
Type A Uncertainty is uncertainty from the data — statistically evaluated measurement uncertainty derived directly from repeated observations.