
Evaluating uncertainty components: Type B A Type B evaluation of standard uncertainty is usually based on scientific judgment using all of the relevant information available, which may include:
Below are some examples of Type B evaluations in different situations, depending on the available information and the assumptions of the experimenter. Broadly speaking, the uncertainty is either obtained from an outside source, or obtained from an assumed distribution.
Multiple of a standard deviation
Procedure: Convert an uncertainty quoted in a handbook, manufacturer's specification,
calibration certificate, etc., that is a stated multiple of an
estimated standard deviation to a standard uncertainty by dividing
the quoted uncertainty by the multiplier.
Confidence interval
Procedure: Convert an uncertainty quoted in a handbook, manufacturer's specification,
calibration certificate, etc., that defines a "confidence interval"
having a stated level of confidence, such as 95 % or 99 %, to
a standard uncertainty by treating the quoted uncertainty as if
a normal probability distribution had been used to calculate it
(unless otherwise indicated) and dividing it by the appropriate
factor for such a distribution. These factors are 1.960 and 2.576
for the two levels of confidence given. 
Normal distribution: "1 out of 2"
Procedure: Model the input quantity in question by a normal probability distribution
and estimate lower and upper limits a_{ }and a_{+ }such that the best estimated value of the input quantity is (a_{+} + a_{})/2 (i.e., the center of the limits) and there is 1 chance out
of 2 (i.e., a 50 % probability) that the value of the quantity
lies in the interval a_{ }to a_{+}. Then u_{j }is approximately 1.48 a, where
Normal distribution: "2 out of 3"
Procedure: Model the input quantity in question by a normal probability distribution
and estimate lower and upper limits a_{ }and a_{+ }such that the best estimated value of the input quantity is (a_{+} + a_{})/2 (i.e., the center of the limits) and there are 2 chances out
of 3 (i.e., a 67 % probability) that the value of the quantity
lies in the interval a_{ }to a_{+}. Then u_{j }is approximately
Normal distribution: "99.73 %"
Procedure: If the quantity in question is modeled by a normal probability
distribution, there are no finite limits that will contain 100
% of its possible values. However, plus and minus 3 standard deviations
about the mean of a normal distribution corresponds to 99.73 %
limits. Thus, if the limits a_{ }and a_{+ }of a normally distributed quantity with mean (a_{+} + a_{})/2 are considered to contain "almost all" of the possible values
of the quantity, that is, approximately 99.73 % of them, then
u_{j }is approximately
Uniform (rectangular) distribution
Procedure: Estimate lower and upper limits a_{ }and a_{+ }for the value of the input quantity in question such that the
probability that the value lies in the interval
Triangular distribution
The rectangular distribution is a reasonable default model in the absence of any other information. But if it is known that values of the quantity in question near the center of the limits are more likely than values close to the limits, a normal distribution or, for simplicity, a triangular distribution, may be a better model.
Procedure: Estimate lower and upper limits a_{ }and a_{+ }for the value of the input quantity in question such that the
probability that the value lies in the interval a_{ }to a_{+ }is, for all practical purposes, 100 %. Provided that there is
no contradictory information, model the quantity by a triangular
probability distribution. The best estimate of the value of the
quantity is then (a_{+} + a_{})/2 with u_{j }=
Schematic illustration of probability distributions
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