CRIM 2653 Lecture Notes - Lecture 8: Ian Hacking

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16 Aug 2016
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Assignment:
-social constructionism and empiricist position taken by Hacking
-social constructivists (reject notions of objectivity, relativism, etc)
-what do social constructivists sacrifice?
-what does Ian Hacking give up?
Damned Lies and Statistics:
Verification and Bad Statistics:
-his ideas are based on this principle: in statistics, the validity and value of statistics are all front-
end loaded
-research and survey designs are all done prior to it
-if mistakes are made in the design of the survey, the results will not be valid
-the problem is that for most us it’s hard to distinguish between good and bad statistics
-(page 4): bad statistics live on, they take on lives of their own
^-if the results get popular no matter how false their study, it takes away the aura of scientific
legitimacy because it’s expressed in numbers
-it would be statistically flawed in a number of ways (Muslim example)
-people generate numbers to justify their ideologies and ideological positions, that doesn’t mean
that it is true
-if the premises and designs are flawed, the results will be flawed
-the numbers are still generated from the computer, irrespective of its validity
-so much of what we take to be empirical evidence of some kind of problem is based on flawed
reasoning
-2 of the biggest problems when one thinks of research designs:
-this is where the problem of “garbage in” and “garbage out” becomes so important
1) language: the problem with surveys or general statistical studies isn’t always that
researchers ask the wrong questions, but they word it improperly (they can ask it in ways
that limit the possible answers..employing this type of method would produce results that
are skewed)..if you limit the number of possible responses, it is difficult to argue that the
answers you received are representative or reflective of the context
2) understanding things in context: if we’re asking people about their levels of fear of crime,
we must take into consideration the context of it (example: attacks on Paris)..the
statistical likelihood of their actually being victimized is low, but their level of fear is high
because of the events..they feel as if there is a real objective risk
-language counts in terms of the way that we ask our questions and our questions should be
asked in a way that is contextually appropriate
-this kind of bad statistics is what the media drives on
-the fact that the numbers were is no verification is that the researcher has discovered anything
factual about the way things are
-there is a difference between making numbers work and identifying the elements of social
reality
Paradox of Statistics:
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