I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.
As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.
So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?
For the first picture to be a type 1 error, H0 (null hypothesis) should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.
However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.
I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)
But based on the Chart given below, that doesn't seem to make any sense.
So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?