Contrast and convergence error: these two errors occur when samples that have strong differences are evaluated together. Let’s make an example with cars. It would not make sense to ask the judges to compare a Ferrari F40 sports car with a Fiat 500 city car. Yes, they are both beautiful, they share the Italian origin and the fact that they are, in their respective category, iconic and easily recognisable items. But the evaluation of the two would be meaningless, because the two are so different (for specifications, price, etc.) that any comparison would be of no use. This is a contrast error. On the other hand, if we added an ugly, unpopular, unsuccessful medium car to the samples, i.e. the Fiat Duna, it would probably have a convergent effect for the Ferrari and the 500, showing more common traits to the latter two cars than there should be by nature.