Yes, a t test may be enough.  Students should also learn that results 
obvious on their face need no stat analy at all -- don't gild the lily.

On 22 Mar 2002, Brad Anderson wrote:

> [EMAIL PROTECTED] (nothanks) wrote in message 
>news:<[EMAIL PROTECTED]>...
> > [EMAIL PROTECTED] (Penley, Julie) wrote in message 
>news:<[EMAIL PROTECTED]>...
> > 
> > Mercy, I hope y'all aren't aiming this at the original poster.
> > Anyways, maybe part of the "problem" is that some fields think
> > they do need "fancy" statistics and models (you know. 
> > "I NEED P-VALUES, GIMME P-VALUES", etc.) when, IMO, simple
> > summaries will suffice.
> 
> [snip]
> 
> I recall serving as an external member on a Ph.D. committee.  The
> student had a pretty well designed randomized experiment with only 2
> comparison groups.  The response variable was continuous and
> approximately normally distributed.  When the student proposed using a
> t-test to compare means, one of the committee members said something
> to the effect of:  "This is a Ph.D. dissertation, you can't use a
> t-test.  You have to use something more sophisticated like ANOVA."
> Ouch.  I cringed, bit my tongue, and tactifully defened the student's
> proposed method of analysis.  A perceived need for statistical
> sophistication and lengthy results sections shouldn't drive research
> questions.  Some important research questions can be correctly
> addressed using very simple methods of analysis.  To refer back to the
> original question, if a 2 x 3 ANOVA is the appropriate analytical
> procedure, then it's the correct procedure.  I do agree with those who
> suggested presenting results exploring the distributional properties
> of the response variable.  The student could certainly present results
> of diagnostic statistics defending the choice of statistic.  Depending
> on the nature of the research question, the student might test for
> interaction, test for simple main effects, and use appropriate
> procedures for multiple comparisons.
> .
> .
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