Error is assumed to be normally distributed in most models.

For example, consider a regression analysis model with normally distributed errors.

It also fails to hold in general if scores are not normally distributed.

As a result, the logarithm of city size is normally distributed.

The problem stems from the fact that the differences are unlikely to be normally distributed.

It may also perform poorly if the data are not normally distributed.

This challenged the idea that changes in price were normally distributed.

This statistic should be used with a large sample size and normally distributed data.

Also note that we only need the error terms to be normally distributed.

The C test further assumes that each individual data series is normally distributed.