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Key Readings

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  • Crupi, V., Tentori, K. and González, M. (2007): On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science, 74, 229-252.
  • Douglas, H. (2004): The Irreducible Complexity of Objectivity. Synthese 138: 453—473.
  • Douglas, H. (2009): Science, Policy, and the Value-free Ideal. Pittsburgh: University of Pittsburgh Press.
  • Fisher, R.A. (1956): Statistical Methods and Scientific Inference. New York: Hafner.
  • Fitelson, B. and C. Hitchcock (2011). Probabilistic Measures of Causal Strength. In P. M. Illari, F. Russo, and J. Williamson (eds.), Causality in the Sciences. Oxford: Oxford University Press.
  • Gelman, A., and C. Shalizi (2012): Philosophy and the Practice of Bayesian Statistics. British Journal of Mathematical and Statistical Psychology 66: 8—18.
  • Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics33(5), 587-606.
  • Good, I.J. (1983/2009): Good Thinking: The Foundations of Probability and its Applications. Reprinted in 2009. New York: Dover.
  • Icard, T. F., Kominsky, J. F., & Knobe, J. (2017). Normality and actual causal strength. Cognition161, 80-93.
  • Ioannidis, J.P.A. (2005): Why Most Published Research Findings Are False. PLOS Medicine 2: e124.
  • Jeffreys, H. (1998). The theory of probability. OUP Oxford.
  • Lipton, P. (2004): Inference to the Best Explanation. Cambridge: Cambridge University Press. Second Edition.
  • Lombrozo, T. & S. Carey  (2006). Functional explanation and the function of explanation. Cognition 99: 167–204.
  • Longino, H. (1990): Science as Social Knowledge. Values and Objectivity in Scientific Inquiry. Princeton: Princeton University Press.
  • Mayo, D.G. (1996): Error and the Growth of Experimental Knowledge. Chicago: Chicago University Press
  • Megill, Allan (1994, ed.): Rethinking Objectivity. Durham/NC: Duke University Press.
  • Pearl, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.
  • Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science349(6251), aac4716.
  • Rosenthal, R. (1979): The file drawer problem and tolerance for null results. Psychological Bulletin 86: 638–641.
  • Royall, R. (1997): Statistical Evidence – A Likelihood Paradigm. Chapman & Hall, London.
  • Schupbach, J., and Sprenger, J. (2011): The Logic of Explanatory Power. Philosophy of Science 78: 105–127.
  • Wagenmakers, E. J. (2007). A practical solution to the pervasive problems ofp values. Psychonomic bulletin & review14(5), 779-804.
  • Wilholt, T. (2009): Bias and Values in Scientific Research. Studies in History and Philosophy of Science 40: 92–101.
  • Williamson, J. (2010): In Defence of Objective Bayesianism. Oxford: Oxford University Press.

 

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