I always get confused regarding the False Positive and False Negative error, is there an easy way to remember it?
Using terms “reject” and “Ho” (null hypothesis), the type I error is rejecting Ho when Ho is true. On the other hand, type II error is accepting Ho when Ho is false.
The “false” term in false positive and false negative refers to the idea that we have committed an error.
The “positive” here is for the rejection of the null hypothesis. Once we reject the null hypothesis, we follow the alternative hypothesis which is a statement of “change” or difference. When we say false positive, we followed the statement of “change” when doing it is actually an error.
On the other hand, the “negative” term refers to “no change” hypothesis. We followed the hypothesis of “no change” (that is the null hypothesis) when in reality there is a change. Hence the term “false” because it is an error.
Hope this helps.
The 365 Team
Thank You Very much team for this great answer. Conceptually I have got the concept. But while applying on real dataset I am getting some confusion. So, request you to explain using any real-life example.