[R] Inference for R Spam

[David Winsemius]

I mostly agree with you, Rolf (and Gunter). I would challenge your  
joint use of the term "scientists". My quibble arises not regarding  
biomedical practitioners (who may be irredeemable as a group)  but  
rather regarding physicists. At least in that domain, I believe those  
domain experts are at least as likely, and possibly more so, to  
understand issues relating to randomness as are statisticians.  
Randomness has been theoretically embedded in the domain for the last  
90 years or so.

-- 
David Winsemius, MD


--
On Mar 4, 2009, at 6:43 PM, Rolf Turner wrote:

>
> On 5/03/2009, at 12:13 PM, Bert Gunter wrote:
>
>>
>> "The purpose of the subject or discipline ``statistics'' is in  
>> essence
>> to answer the question ``could the phenomenon we observed have arisen
>> simply by chance?'', or to quantify the *uncertainty* in any estimate
>> that we make of a quantity."
>>
>>
>> May I take strong issue with this characterization? It is far too  
>> narrow and
>> constraining. We are scientists first and foremost. The most  
>> important and
>> useful thing I do is to collaborate with other scientists to frame  
>> good
>> questions, design good experiments and studies, and gain insight  
>> into the
>> results of those experiments and studies (usually via graphical  
>> displays,
>> for which R is superbly suited). Blessing data with P-values is  
>> rarely of
>> much importance, and is often frankly irrelevant and even  
>> misleading (but
>> that's another rant).
>>
>> George Box said this much better than I: "The business of the  
>> statistician
>> is to catalyze the scientific learning process."
>>
>> This is much much more than you intimate.
>
> I must respectfully disagree.  Far be it from me to argue with  
> George Box,
> but nevertheless ... it may be statisticians *business* to catalyze  
> the
> scientific learning process, but that is the business of *any*  
> scientist.
> What we bring to the process is our understanding of the essentials of
> statistics, just as the chemist brings her understanding of the  
> essentials
> of chemistry and the biologist her understanding of the essentials of
> biology.
>
> The essentials of statistics consist in answering the question of  
> ``could
> this phenomenon have arisen by chance?''  This is where we  
> contribute in a
> way that other scientists do not.  They don't understand  
> variability, the
> poor dears.  (Unless they have been well taught and thereby have  
> become
> in part statisticians themselves.) They have a devastating tendency  
> to treat
> an estimated regression line as *the* regression line, the truth.   
> And so on.
>
> The *way* we address the question of ``could it have happened by  
> chance''
> and the way we address the problem of quantifying variability is  
> indeed open
> to a broad range of techniques including graphics.
>
> Note that I did not say word one about p-values.  The example I gave  
> was
> a scientific question --- is there a difference in the home field  
> advantage
> between the English Premier Division and the equivalent division or  
> league
> in Italy?  How much of a difference?  You may wish to throw in a p- 
> value,
> or you may not.  You will probably wish to look at a confidence  
> interval.
> You may wish to look at the question from the point of view of the  
> distribution
> of (home) - (away) differences, in which case graphics will most  
> certainly
> help.  But it comes down to answering the basic question.  If you  
> have no
> ability to answer such questions you are not, or might as well not  
> be, a
> statistician.
>
> 	cheers,
>
> 		Rolf Turner
>
>
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