I'm not quite sure what the exact question is.
False Positives (Type I errors) are rejections of true null hypotheses (finding a difference, when actually there is none)
True Negatives are non-rejections of true null hypotheses (not finding a difference, because there actually isn't one)
False Negatives (Type II errors) are non-rejections of wrong null hypotheses (not finding a difference, when actually there is one)
True Positives are rejections of wrong null hypotheses (finding a difference that is actually there)
Does that answer the question?