X-Nico

Chilomastix mesnili

It can create a false positive which would result in unnecessary treatment or a false negative which would withhold necessary treatment.

Heteroscedasticity

For example, if OLS is performed on a heteroscedactic data set, yielding biased standard error estimation, a researcher might fail to reject a null hypothesis at a given significance level, when that null hypothesis was actually uncharacteristic of the actual population (making a type II error).

Probability of error

Type I errors which consist of rejecting a null hypothesis that is true; this amounts to a false positive result.

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Type II errors which consist of failing to reject a null hypothesis that is false; this amounts to a false negative result.

Robust regression

In fact, the type I error rate tends to be lower than the nominal level when outliers are present, and there is often a dramatic increase in the type II error rate.

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Although it is sometimes claimed that least squares (or classical statistical methods in general) are robust, they are only robust in the sense that the type I error rate does not increase under violations of the model.

Windows Genuine Advantage

The WGA program can produce false positives (incorrectly identifying a genuine copy of Windows as "not genuine").

see also