notebooks

 Bronwe 1980 ·          he barely referred to his botanical statistics or the long sequence of calculations which he had undertaken from 1854 to 1858. He compressed and simplified these into a few meager paragraphs, giving his readers only six pages of statistical data to fill out the discussion of "variation under nature" in Chapter 11.2 By contrast, he had originally devoted over fifty tightly written folios, with further supplementary notes and tables, to the same theme in the "big species book," Natural Selection 53 ·          botanical arithmetic (a term coined by Humboldt in 1815)7 consisted merely of counting up all the species in area A and all those in area B, and itemizing how many were held in common. 55 ·          Depois Brown tornou popular o calculo da media de sps por genero 56 ·          the "Sketch" and "Essay" he had asserted that very little variation was seen in a "wild state," and had repeated over and over again th

 The C-word, the P-word, and realism in epidemiology  Alex Broadbent Abstract This paper considers an important recent (May 2018) contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution to this problem: improved restrictions on the meaningful use of causal language, in particular “causal effect”. This paper argues that new restrictions in fact fail their own standards of meaningfulness. The paper portrays the desire for a restrictive definition of causal language as positivistic, and argues that contemporary epidemiolo