I promise a real post, eventually.  But in the mean time, a quote that I love about bad science (bad statistics, really):

 

 

Many of us have been taught that it is technically improper

and perhaps even immoral to analyze and reanalyze

our data in many ways (i.e., to snoop around in the

data). We were taught to test the prediction with one

particular preplanned test and take a result significant at

the .05 level as our reward for a life well-lived. Should the

result not be significant at the .05 level, we were taught,

we should bite our lips bravely, take our medicine, and

definitely not look further at our data. Such a further look

might turn up results significant at the .05 level, results to

which we were not entitled. All this makes for a lovely

morality play, and it reminds us of Robert Frost’s poem

about losing forever the road not taken, but it makes for

bad science and for bad ethics.

 

 

-  Rosenthal, 1994 in Science and Ethics in Conducting, Analyzing, and Reporting Psychological Research

My thoughts exactly.  We spend so much time pretending like statistics are black and white, i.e, if you have this data, then you run this test, and you get this result, and you make this conclusion.  But that’s not the way it should work.  I get so irritated when people deify statistics as if they hold all of this magical power.  I mean, I love stats as much (probably much more, actually) as the next person, but I love them because they are a tool that could potentially lead to practical results, not because they are the practical results themselves (which they’re not).  I mean, seriously, is a p-value of .05 really that different from .06?  What is it that makes you celebrate your results if you have 95% confidence in them, as opposed to 94%?