Here is what the author had to say...

There's a little more to this than meets the eye.

Conventionally, most authors call Type I errors false negative, and

Type II false positive; but I do see some authors who define them the

other way round.

I think the reason for this is that in hypothesis testing, we are

strictly speaking testing the null hypothesis, ie. the hypothesis that

there is no difference, and when we reach a positive conclusion about

the null hypothesis, we are reaching a negative conclusion about the

phenomenon we are studying.

For example, let's say we are testing a drug vs. a placebo.

Let's say that we find that there is no difference between the drug and

the placebo; the null hypothesis is that there is no difference, so we

accept the null hypothesis.

If in reality this is a mistake, we have made a false positive (type

II) error (we have reached a positive conclusion, but it is a false

one).

But some authors would say that a study which reaches a conclusion that

there IS a significant difference is a "positive" study, and one that

concludes that there is no difference (eg. between a drug and a

placebo) is a "negative" study. (This sounds like common sense, but you

have to remember that strictly speaking in statistics we are testing a

null hypothesis, not a drug).

An example of this is shown on the American College of Physicians

online primer at

http://www.acponline.org/journals/ecp/novdec01/primer_errors.htm (and

the table it is linked to)

where they say "A type I error is analogous to a false-positive result

during diagnostic testing: A difference is shown when in "truth" there

is none." - but this is only because they are taking the "common sense"

idea that a "positive" result is one that shows a "difference"

- and if you look at the table which the page links to, you see that a

type I error is one where you conclude there is no "difference" when in

fact there is one; which is the same as I am saying, except they

confuse us by referring this to a "positive" study!

It sure is confusing. But basically everyone agrees that a type I is

rejecting the null hypothesis when it is true, and a type II is

accepting a null hypothesis that is false; the "false positive" or

"false negative" terminology really depends on whether you are

referring to the null hypothesis or the alternative hypothesis.