Oh boy, you've opened up a can of worms with this one!
A short summary is the line from Jacob Cohen in his book on multiple regression "This is a subject on which reasonable people can differ".
My own view is that people concern themselves with this far too much; but this is a symptom of a general vast over-emphasis of p--values and significance testing. My favorite professor in grad school used to say "Stop p-ing on the research!". Remember what a p-value is: It is a measure of how often, if you do totally ridiculous things, you will get significant results. More technically, it is the proportion of times you will get a test statistic as large or larger than the one you got in the sample you had, if the real effect in the population from which the sample was drawn was 0.
Unless you are on a total fishing expedition, this is a largely irrelevant question. If you are on a total fishing expedition, you have other problems to cope with! :-)
Also remember that, when you lower type 1 error (e.g. by correcting for multiple comparisons) you necessarily raise type 2 error. Which is worse? The default criteria are type 1 = 0.05 (or 0.01 sometimes) and type 2 = .2 (or .1 sometimes) (that is, power = .8 or .9). Is a type 1 error 4 times worse than a type 2 error? It depends.
Then, if you DO decide to correct for multiple comparisons, you have the very vexed question of how many comparisons you should correct for. All the analyses in one table? One article? One area of research? Or what? For example, if 100 people look at the relationship between social security number and weight, then you should correct for ALL 100, even the ones you don't know about!
In short, I think corrections for multiple comparisons are rarely needed.
Rather than evaluate research this way, I like Robert Abelson's MAGIC criteria: Magnitude, Articulation, Generality, Interestingness and Credibility. I wrote more about this in my review of Abelson's book:
http://www.statisticalanalysisconsulting.com/book-review-statistics-as-principled-argument-by-robert-abelson/ -------------------------------------------
Peter Flom
-------------------------------------------