Ordinary linear regression predicts the expected value of a given unknown quantity (the response variable, a random variable) as a linear combination of a set of observed values (predictors). This implies that a constant change in a predictor leads to a constant change in the response variable (i.e. a linear-response model). This is appropriate when the response variable has a normal distribution.
So, Wikipedia makes an assumption about the response variable, namely that it is normally distributed. However, in other sources and here in stack exchange the normality is required for the error terms. If they are not normally distributed we should go for some generalized linear model.
What is the Wikipedia article referring to or is it wrong?