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Wednesday, June 01, 2005

The appliedness of Statistical Research

Statistics is an interdisciplinary formal information science. As any science, there are something called "theoretical statistics" and something called "applied statistics". I think I am more on the applied side. But what makes a statistical research "applied" without turning it into a service to another scientist?

To me, a young statistician, there seems to be a thin (very very thin) line between "applied research" and "consulting/application". For an academic statistician, crossing the line by doing service-like application for other scientists can by harmful I suppose (or, I guess). But how far inside, from this line, of the applied research domain is safe enough? What decides the appropriate appliedness of a project?

Since there is not absolute standard for appropriate appliedness for the research projects (am I missing something here), one (who are dedicated to do some applied research) is faced with two choices: make an applied research more applied to address the needs of the other discipline or make an applied research less applied to maintain some statistical intellectual merits. Sometime, one does not have to make a choice. That is just the dreamland for an applied statistician.

4 comments:

Nick Payne said...

This is somewhat only tangential to your comment. After years of being a client of statisticians and then becoming one, I am struck by something. Most scientistis and researchers who generate data understand that their data is only some approximation of the variable they are truly studying, while most consulting statisticans acti=as if the data WERE the variable of interest.

How can I get applied statisticians to look past the data towards the actual variable? Issues in this are often that the measuring instrument introduces (or subtracts) attributes that are not releveant to thr true underlying variable.

PS I went to Columbia in the early '60's

Nick Payne said...

This is somewhat only tangential to your comment. After years of being a client of statisticians and then becoming one, I am struck by something. Most scientistis and researchers who generate data understand that their data is only some approximation of the variable they are truly studying, while most consulting statisticans acti=as if the data WERE the variable of interest.

How can I get applied statisticians to look past the data towards the actual variable? Issues in this are often that the measuring instrument introduces (or subtracts) attributes that are not releveant to thr true underlying variable.

PS I went to Columbia in the early '60's

Anonymous said...

I agree with your sentiments. As an instructor I remind my student they are working in the area of approximation with inferential statistics.

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