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The contaminated normal distribution is a special form of mixed distribution . It plays a major role in robustness studies of estimators and tests .
The real random variable has a contaminated normal distribution if its density function is in the form


with , i.e. as a convex combination of two normal distribution density functions.
![{\ displaystyle \ varepsilon \ in [0,1]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/d472b1b7207f57be05aaefccd6651d486dd59a8c)
The distribution function then has the form
-
.
The distribution function of a normally distributed random variable applies here .

The following applies to the expected value and the variance :
,
.
Often, through additional conditions, such as special cases are derived ( scale-contaminated normal distribution ).

example
A manufacturer of electronic devices uses capacitors with a capacity of 5 nF, which it purchases from two manufacturers. Those manufactured by A show a slightly smaller scatter than those from B. 60% of the capacitors purchased come from manufacturer A, 40% from B. Assume that the capacitance of the capacitors from both manufacturers is normally distributed with parameters in a sufficiently wide range . Be and .



A deviation of more than 10% from the nominal value of the capacitance is highly undesirable. What is the probability that a capacitor has a capacitance that differs by more than 10%?
A proportion of around 0.000361849 of all capacitors shows a deviation of more than 10% in terms of capacity.