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st: Inferential Statistics with population data


From   David Greenberg <dg4@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Inferential Statistics with population data
Date   Mon, 01 Jun 2009 15:59:42 -0400

In 1970 Aldine Pub. Co. published THE SIGNIFICANCE TEST CONTROVERSY: A READER, edited by Denton Morrison. The papers included in the volume debate the issue. 
 - David Greenberg, Sociology Department, New York U. 

----- Original Message -----
From: Salima Bouayad Agha <Salima.Bouayad-Agha@univ-lemans.fr>
Date: Saturday, May 30, 2009 4:17 pm
Subject: Re: st: Explaining the Use of Inferential Statistics Even Though I Have Population Data
To: statalist@hsphsun2.harvard.edu


> Well, even if is not really a stata question, I Think that the  
> question of your reviewer is not obvious.
> In statistics a sample can effectively be a part of the population,  
> and it is the most useful and current way to talk about of a sample,  
> 
> but in mathematical statistical course students also learn that a  
> sample can be just the population a one specific moment of time and  
> that this population can be very different if something else (random  
> 
> phenomenon) happens. Think for example of time series or some  
> staistical process, let for example talk about the population of  
> computers produced by a firm. So I'm not sure that your answer is the  
> 
> best one, depending on which field of research you are working. Just  
> 
> take a few moment to review some theoretical books on xaht is a sample 
>  
> in statistics.
> 
> Salima
> 
> PS :
> In mathematical terms, given a random variable X with distribution F,  
> 
> a sample of length n\in\mathbb{N} is a set of n independent,  
> identically distributed (iid) random variables with distribution F. It 
>  
> concretely represents n experiments in which we measure the same  
> quantity. For example, if X represents the height of an individual and 
>  
> we measure n individuals, Xi will be the height of the i-th  
> individual. Note that a sample of random variables (i.e. a set of  
> measurable functions) must not be confused with the realisations of  
> these variables (which are the values that these random variables  
> take). In other words, Xi is a function representing the mesure at the 
>  
> i-th experiment and xi = Xi(?) is the value we actually get when  
> making the measure.
> 
> 
> 
> 
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