Which type of distributions is typically used for Type A evaluations of uncertainty?

Type A evaluations of uncertainty are primarily based on statistical methods that involve a series of measurements. In these evaluations, Gaussian distributions are most relevant because they are the foundation of statistical inference for normally distributed data. The central limit theorem supports the use of Gaussian distributions, suggesting that the distribution of the sample means tends to be normal regardless of the shape of the data distribution, as long as the sample size is sufficiently large.

In measurement and uncertainty analysis, many measurement errors occur naturally due to random fluctuations and can often be modeled as Gaussian or normally distributed. This allows for the incorporation of standard statistical tools and techniques, such as calculating confidence intervals and performing hypothesis tests, which are essential for interpreting the reliability of measured values.

Other distribution types like uniform, exponential, or rectangular distributions are not generally suitable for Type A evaluation since they do not appropriately capture the characteristics of measurement errors commonly encountered in practical applications. Uniform distributions imply constant probability across a range, which does not reflect the typical variations in measurements. Exponential distributions are used primarily to model time until an event occurs rather than errors in measurement. Rectangular distributions, while similar to uniform, suggest a constant probability over an interval that doesn’t adequately model the randomness of measurement errors typically represented by a Gaussian approach.