So we make two types of errors: a type I error, or false positive, is believing a pattern is real when it is not; a typeIIerror, or false negative, is not believing a pattern is real when it is.
In contrast, if he infers that she is not interested when she is in fact interested, then he has made an error of false negative (what the statisticians call the "TypeII" error).