Herman Rubin wrote:

> The problem is that you are trying to teach statistics
> without the background needed to UNDERSTAND it.  Without an
> understanding of probability concepts, NOT how to calculate
> probabilities in simple problems, no statistical procedure
> "makes sense".  Means, medians, variances, significance 
> tests, confidence intervals, etc., are dei ex machinae.
> 
> The worst of all consists of transformations to normality.
> This destroys all structure, and in the real world, nothing
> is normal.  Correlations are much harder to do inference
> with than covariances; statistics is not a collection of
> religious mantras, but is the problem of decision making
> under uncertainty.  It must be modeled as such.

Over the years, Prof Rubin has made this statement repeatedly.
I believe it. I see how those without the understanding he speaks of 
have great difficulty integrating concepts and how they fail to see 
connections that such understanding makes obvious.

There is another reality we must face as educators.  There are more data 
sets than trained statisticians to analyze them, while fewer people are 
choosing statistics as a career.

During WWII, the need for medical doctors was so great that three year 
programs were developed to keep that profession staffed.  Without being 
overly dramatic, statistics is in a similar situation.  Prof Rubin 
reminds us of the dangers of shortcuts, but not all shortcuts are need 
be dangerous.  I believe many of these introductory courses are 
attempts to get people to the front lines ASAP.  They will necessarily 
be inadequate in some respects.  What is needed is a systematic study 
and development of ways to integrate the two approaches to produce, if 
not the next generation of statisticians, then a new generation of 
analysts who might work in close association with a statistician.
.
.
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