Thanks again Jeremy.
In answer to your question about whether I'd claim a null result with
means in the wrong direction, the answer is no. In my current research I
have taken to approach of not running a t-test on groups with means in
the wrong direction, but then commented that say males scored higher
than females, thus, no further statistical analysis was conducted. Then
I have briefly discussed this finding the discussion (but only in the
context of mean scores and not statistical sig., obviously). Do you
agree with this approach (given a 1 tailed test)? I suppose ideally you
are saying use 2 tailed tests, which I assume would address this
problem.
I like your definition of conditions for a 1 tailed test. Why wasn't
this given out to me years ago at undergrad level? Just out of interest,
do you have a text reference for this kind of approach to defining 1
tailed tests? I'd like to read more as none of my books or hundreds of
stats papers seem to adopt this approach and google also fails me :-(
Nothing like a bit of bed time stats reading, though I have to admit I
like stats (shall I lock my doors now? :-)
I don't know Patrick McGhee (he must've left the dept a while back)
though the name rings a bell. I've been at UCLan nearly 6 years now and
don't recall him being a staff member. But then you finished your PhD a
while back didn't you.
Kathryn
>>> Jeremy Miles <[log in to unmask]> 22/03/2007 20:38:46 >>>
On 22/03/07, Kathryn Jane Gardner <[log in to unmask]> wrote:
> Thanks Jeremy for answering my questions. Just to clarify though, I
was
> using directional to refer to 1 tailed tests (slip in terminology as
I
> realise that these aren't necessarily the same thing, though they
are
> often used synonymously). Someone in my dept said that if you run a
1
> tailed test (say a t test) and the means in are in the wrong
direction,
> then the t test shouldn't be run i.e., you inspect the group means
first
> and then only run t tests if results are in the direction you
predicted.
> I think approach is consistent with what you were saying about not
> reporting a sig result if it is in the wrong direction. I think?
>
That's true, but if the means are in the wrong direction, would you
*really* say that you have found nothing.
Let's say that you do a test of intelligence on black and white
children. All the evidence (that I know of) would suggest that, if
you find a difference, it would be that the black children should
score lower.
So you run the test, and you find that the black children score
significantly higher. Do you then say "Well, that's a null result. I
found no effect."?
> I do see your point re: 1 tailed tests, and you clearly don't see a
lot
> of them in the papers you review. You said "You can make a
directional
> prediction based on anything. But if you then use that directional
> prediction to argue that you can do a one tailed test, then that's
(in
> my opinion) naughty." I think like many, I have assumed that a 1
tailed
> test is used when a directional prediction is made and there is
enough
> theory and/or evidence to do so. But it seems you don't agree with
this
> and do not advocate using 1 tailed tests. As I said earlier, I
haven't
> come across the use of 2 tailed tests for directional predictions.
Maybe
> I am missing the basic underlying principles of the use of 1 and 2
> tailed tests and how they differ from directional and
non-directional
> tests, but if am then so are many of my colleagues! So...if you
could
> define the conditions for a 1 tailed test to be run, what would they
> be?
>
A one tailed test should be used when an effect in the opposite
direction to that which was expected would theoretically equivalent to
a zero effect.
Jeremy
P.S. Do you know Patrick McGhee, at UCLAN? He was my PhD supervisor.
--
Jeremy Miles
Learning statistics blog: www.jeremymiles.co.uk/learningstats
|