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Data-driven science is a failure of imagination

February 10, 2013


From the site:

Rosling is right that data are important and that science uses statistics to deal with the data. But he completely ignores the second component of statistics: hypothesis (here equivalent to model or theory). There are two ways to define statistics and both require data as well as hypotheses: (1) Frequentist statistics makes probabilistic statements about data, given the hypothesis. (2) Bayesian statistics works the other way round: it makes probabilistic statements about the hypothesis, given the data. Frequentist statistics prevailed as a major discourse as it used to be computationally simpler. However, it is also less consistent with the way we think – we are nearly always ultimately curious about the Bayesian probability of the hypothesis (i.e. “how probable it is that things work a certain way, given what we see”) rather then in the frequentist pobability of the data (i.e. “how likely it is that we would see this if we repeated the experiment again and again and again”).


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