Philosophy of statistics

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The philosophy of statistics involves the meaning, justification, utility, use and abuse of statistics and its methodology, and ethical and epistemological issues involved in the consideration of choice and interpretation of data and methods of Statistics.

  • David Cox makes the point that any kind of interpretation of evidence is in fact a statistical model, although it is known through Ian Hacking's work that many are ignorant of this subtlety.
  • Issues arise involving sample size, such as cost and efficiency, are common, such as in polling and pharmaceutical research.
  • Extra-mathematical considerations in the design of experiments and accommodating these issues arise in most actual experiments.
  • Leo Breiman exposed the diversity of thinking in his article on 'The Two Cultures', making the point that statistics has several kinds of inference to make, modelling and prediction amongst them.[2]
  • Objectivity in statistics is often confused with truth whereas it is better understood as replicability, which then needs to be definied in the particular case. Theodore Porter develops this as being the path pursued when trust has evaporated, being replaced with criteria.[3]
  • Ethics associated with epistemology and medical applications arise from potential abuse of statistics, such as selection of method or transformations of the data to arrive at different probability conclusions for the same data set. For example, the meaning of applications of a statistical inference to a single person, such as one single cancer patient, when there is no frequentist interpretation for that patient to adopt.
  • Campaigns for statistical literacy must wrestle with the problem that most interesting questions around individual risk are very difficult to determine or interpret, even with the computer power currently available.

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Notes[edit]

  1. ^ Hacking, Ian (2006)The Emergence of Probability, 2nd Ed
  2. ^ Breiman, Leo (2001). "Statistical Modeling: The Two Cultures". Statistical Science 16 (3): 199–231. doi:10.1214/ss/1009213726. 
  3. ^ Porter, Theodore M (1995)Trust in Numbers