The Predicament of Truth: On Statistics, Causality, Physics, and the Philosophy of Science

  • Richard J. C. M. Starmans
Part of the Springer Series in Statistics book series (SSS)


On the one hand, the first part of this essay’s title may seem a little querulous and ill-omened, a prelude to a litany of complaints or to sorrowful, grief-stricken pondering on the deterioration of the quest for truth. Undeniably, it suggests a further decline of civilization as we know it, in accordance with Oswald Spengler’s pessimistic anticipations on the lifespan of civilizations, published in Der Untergang des Abendlandes in 1918 at the end of the First World War. The concept of truth has been essential in the history of ideas and characteristic and distinguishing for the human condition. Even if one adheres to Protagoras’ homo-mensura-principle, be it in a mitigated or radical manner, people cannot exist, survive or function without proclaiming, stipulating, conjecturing, establishing or cherishing a notion of truth, underlying and motivating their thoughts, words and deeds. As such it has been ubiquitous in religion, metaphysics, epistemology, science, politics and everyday life. From a historical-philosophical point of view the concept of truth is pivotal in epistemology; it precedes, subsumes or—at the least—it is presupposed by concepts like knowledge, rationality, objectivity, causality, justification, inference and many more. At the same time truth may easily be denied, distorted, declared obsolete, or conveniently modified and relativized on behalf of self-interest, religion, political ideology, freedom, stakeholders interests, public health, national security, climate, the Will of the People, et cetera. This fragility of truth may be noticeable in politics, journalism (whether phrased as disinformation, alleged truisms or fake news), on social media, in historiography or—horribile dictu—even in philosophy and modern science.


  1. C. Anderson, The end of theory: the data deluge makes the scientific method obsolete. Wired (2008)Google Scholar
  2. P. Galison, How Experiments End (University of Chicago Press, Chicago, 1987)Google Scholar
  3. A. Gelman, C. Shalizi, Philosophy and the practice of bayesian statistics. Br. J. Math. Stat. Psychol. 66(1), 8–38 (2013)MathSciNetCrossRefGoogle Scholar
  4. G. Gigerenzer, The Empire of Chance: How Probability Changed Science and Everyday Life (Cambridge University Press, Cambridge, 1989)CrossRefGoogle Scholar
  5. I. Hacking, The Emergence of Probability (Cambridge University Press, Cambridge, 1975)zbMATHGoogle Scholar
  6. I. Hacking, The Taming of Chance (1990) (Cambridge University Press, Cambridge, 1990)CrossRefGoogle Scholar
  7. L. Krüger, L. Daston, M. Heidelberger, G. Gigerenzer, M.S. Morgan, The Probabilistic Revolution. (MIT Press, Cambridge, 1987)Google Scholar
  8. D. Mayo, Error and the Growth of Experimental Knowledge (University of Chicago Press, Chicago, 1996)CrossRefGoogle Scholar
  9. D. Mayo, Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (Cambridge, Chicago, 2010)zbMATHGoogle Scholar
  10. C. O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown Publishing Group, New York, 2016)Google Scholar
  11. J. Pearl, Causality: Models, Reasoning, and Inference, 2nd edn. (Cambridge, New York, 2009a)Google Scholar
  12. I. Phyllis, F. Russo. Causality; Philosophical Theory meets Scientific Practice (Oxford University Press, Oxford, 2016)Google Scholar
  13. T.M. Porter, The Rise of Statistical Thinking (Princeton University Press, Princeton, 1986)Google Scholar
  14. T.M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton University Press, Princeton, 1995)Google Scholar
  15. R.J.C.M. Starmans, Models, inference, and truth: probabilistic reasoning in the information era, in Targeted Learning: Causal Inference for Observational and Experimental Data, ed. by M. van der Laan, S. Rose (Springer, Berlin, 2011)Google Scholar
  16. R.J.C.M. Starmans, The reality behind the model and the cracks in the mirror of nature (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 21 (Garant Publishers, Antwerpen, Apeldoorn, 2011a)Google Scholar
  17. R.J.C.M. Starmans, Ethics and statistics; the progress of a laborious dialogue (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 22 (Garant Publishers, Antwerpen, Apeldoorn, 2012a)Google Scholar
  18. R.J.C.M. Starmans, Statistics, discomfort and the human dimension (in Dutch), in STAtOR, vol. 13 (2012b)Google Scholar
  19. R.J.C.M. Starmans, The world of values; statistics, evolution and ethics (in Dutch), in STAtOR, vol. 13 (2012c)Google Scholar
  20. R.J.C.M. Starmans, Idols and ideals; francis bacon, induction and the hypothetico-deductive model (in Dutch). in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 23 (Garant Publishers, Antwerpen, Apeldoorn, 2013)Google Scholar
  21. R.J.C.M. Starmans, Between hobbes and turing; george boole and the laws of thinking (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 25 (Garant Publishers, Antwerpen, Apeldoorn, 2015a)Google Scholar
  22. R.J.C.M. Starmans, With google toward the automatic statistician (in Dutch), in STAtOR, vol. 16 ( 2015b)Google Scholar
  23. R.J.C.M. Starmans, Shannon; information, entropy and the probabilistic worldview (in Dutch), in Filosofie Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 26 (Garant Publishers, Antwerpen, Apeldoorn, 2016a)Google Scholar
  24. R.J.C.M. Starmans, The advent of data science - some considerations on the unreasonable effectiveness of data, in Handbook of Big Data - Handbooks of Modern Statistical Methods, ed. by P. Buhlmann, P. Drineas, M. Kane, M.J. van der Laan (Chapman & Hall/CRC, New York, 2016b)Google Scholar
  25. R.J.C.M. Starmans, From heraclitus to shannon: the velvet revolution of data in context and flux (in Dutch), in STAtOR, vol. 18 (2017a)Google Scholar
  26. R.J.C.M. Starmans, The end of theory or the unreasonableness of data (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017b), p. 2Google Scholar
  27. R.J.C.M. Starmans, The new house of salomon: Peter galison and the empirical tradition (in Dutch), in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017c), p. 4Google Scholar
  28. R.J.C.M. Starmans, The tryptych of the Bayesian paradigm: confirmation, inference and algoritmics, in Filosofie, Tweemaandelijks Vlaams-Nederlands Tijdschrift, jaargang, vol. 27 (Garant Publishers, Antwerpen, Apeldoorn, 2017d)Google Scholar
  29. S. Stigler, The History of Statistics: The Measurement of Uncertainty Before 1900 (Harvard University Press, Cambridge, MA, 1986)zbMATHGoogle Scholar
  30. S. Stigler, The History of Statistical Concepts and Methods (Harvard University Press, Cambridge, MA, 1999)zbMATHGoogle Scholar
  31. S. Stigler, The Seven Pillars of Statistical Wisdom (Harvard University Press, Cambridge, MA, 2016)CrossRefzbMATHGoogle Scholar
  32. J. Tacq, Causality in qualitative and quantitative research. Qual. Quant. 45(2), 263–291 (2011)CrossRefGoogle Scholar
  33. M.J. van der Laan, S. Rose, Targeted Learning: Causal Inference for Observational and Experimental Data (Springer, Berlin, Heidelberg, New York, 2011)CrossRefGoogle Scholar
  34. M.J. van der Laan, R.J.C.M. Starmans, Entering the era of data science: targeted learning and the integration of statistics and computational data analysis. Adv. Stat. 2014, 502678 (2014)Google Scholar
  35. S. Weinberg, Dreams of a Final Theory: The Scientist’s Search for the Ultimate Laws of Nature (Random House Inc., New York, 1993)Google Scholar
  36. R.J. Wieringa, Design Science Methodology for Information Systems and Software Engineering (Springer, New York, 2014)CrossRefGoogle Scholar
  37. J. Williamson, Probabilistic theories of causality, in The Oxford Handbook of Causation, ed. by H. Beebee, C. Hitchcock, P. Menzies (Oxford University Press, Oxford, 2009), pp. 185–212Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Information and Computing Sciences, Buys Ballot LaboratoryUniversiteit Utrecht, Princetonplein 5UtrechtThe Netherlands

Personalised recommendations