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Hypothetico-deductive model

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Thehypothetico-deductive modelormethodis a proposed description of thescientific method.According to it,scientific inquiryproceeds by formulating ahypothesisin a form that can befalsifiable,using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions.[1]

Example[edit]

One example of an algorithmic statement of the hypothetico-deductive method is as follows:[2]

1.Use your experience: Consider the problem and try to make sense of it. Gather data and look for previous explanations. If this is a new problem to you, then move to step2.
2.Form a conjecture (hypothesis): When nothing else is yet known, try to state an explanation, to someone else, or to your notebook.
3.Deduce predictions from the hypothesis: if you assume2is true, what consequences follow?
4.Test (orexperiment): Look for evidence (observations) that conflict with these predictions in order to disprove2.It is a fallacy or error in one's reasoning to seek3directly as proof of2.Thisformal fallacyis calledaffirming the consequent.[3]

One possible sequence in this model would be1,2,3,4.If the outcome of4holds, and3is not yet disproven, you may continue with3,4,1,and so forth; but if the outcome of4shows3to be false, you will have to go back to2and try to invent anew 2,deduce anew 3,look for4,and so forth.

Note that this method can never absolutelyverify(prove the truth of)2.It can onlyfalsify2.[4](This is what Einstein meant when he said, "No amount of experimentation can ever prove me right; a single experiment can prove me wrong."[5])

Discussion[edit]

Additionally, as pointed out byCarl Hempel(1905–1997), this simple view of the scientific method is incomplete; a conjecture can also incorporate probabilities, e.g., the drug is effective about 70% of the time.[6]Tests, in this case, must be repeated to substantiate the conjecture (in particular, the probabilities). In this and other cases, we can quantify a probability for our confidence in the conjecture itself and then apply aBayesian analysis,with each experimental result shifting the probability either up or down.Bayes' theoremshows that the probability will never reach exactly 0 or 100% (no absolute certainty in either direction), but it can still get very close to either extreme. See alsoconfirmation holism.

Qualification of corroborating evidence is sometimes raised as philosophically problematic. Theraven paradoxis a famous example. The hypothesis that 'all ravens are black' would appear to be corroborated by observations of only black ravens. However, 'all ravens are black' islogically equivalentto 'all non-black things are non-ravens' (this is thecontrapositiveform of the original implication). 'This is a green tree' is an observation of a non-black thing that is a non-raven and therefore corroborates 'all non-black things are non-ravens'. It appears to follow that the observation 'this is a green tree' is corroborating evidence for the hypothesis 'all ravens are black'. Attempted resolutions may distinguish:

  • non-falsifying observations as to strong, moderate, or weak corroborations
  • investigations that do or do not provide a potentially falsifying test of the hypothesis.[7]

Evidence contrary to a hypothesis is itself philosophically problematic. Such evidence is called afalsificationof the hypothesis. However, under the theory ofconfirmation holismit is always possible to save a given hypothesis from falsification. This is so because any falsifying observation is embedded in a theoretical background, which can be modified in order to save the hypothesis.Karl Popperacknowledged this but maintained that a critical approach respecting methodological rules that avoided suchimmunizing stratagemsis conducive to the progress of science.[8]

PhysicistSean Carrollclaims the model ignoresunderdetermination.[9]

Versus other research models[edit]

The hypothetico-deductive approach contrasts with other research models such as theinductive approachor grounded theory. In the data percolation methodology, the hypothetico-deductive approach is included in a paradigm of pragmatism by which four types of relations between the variables can exist: descriptive, of influence, longitudinal or causal. The variables are classified in two groups, structural and functional, a classification that drives the formulation of hypotheses and the statistical tests to be performed on the data so as to increase the efficiency of the research.[10]

See also[edit]

Types of inference[edit]

Citations[edit]

  1. ^Popper, Karl (1959).The Logic of Scientific Discovery.Abingdon-on-Thames: Routledge.
  2. ^Peter Godfrey-Smith (2003)Theory and Reality,p. 236.
  3. ^Taleb 2007e.g., p. 58, devotes his chapter 5 tothe error of confirmation.
  4. ^"I believe that we do not know anything for certain, but everything probably." —Christiaan Huygens,Letter to Pierre Perrault, 'Sur la préface de M. Perrault de son traité del'Origine des fontaines' [1763],Oeuvres Complétes de Christiaan Huygens(1897), Vol.7,298. Quoted in Jacques Roger,The Life Sciences in Eighteenth-Century French Thought,ed. Keith R. Benson and trans. Robert Ellrich (1997), 163. Quotation selected byBynum & Porter 2005,p. 317 Huygens 317#4.
  5. ^As noted by Alice Calaprice (ed. 2005)The New Quotable EinsteinPrinceton University Press and Hebrew University of Jerusalem,ISBN0-691-12074-9p. 291. Calaprice denotes this not as an exact quotation, but as a paraphrase of a translation of A. Einstein's "Induction and Deduction".Collected Papers of Albert Einstein7Document 28. Volume 7 isThe Berlin Years: Writings, 1918-1921.A. Einstein; M. Janssen, R. Schulmann, et al., eds.
  6. ^Murzi, Mauro (2001, 2008), "Carl Gustav Hempel (1905—1997)",Internet Encyclopedia of Philosophy.Murzi used the term relative frequency rather than probability.
  7. ^John W. N. Watkins(1984),Science and Skepticism,p. 319.
  8. ^Karl R. Popper (1979, Rev. ed.),Objective Knowledge,pp. 30, 360.
  9. ^Sean Carroll (3 July 2013)."What is Science?".
  10. ^Mesly, Olivier (2015),Creating Models in Psychological Research,United States: Springer Psychology, p. 126,ISBN978-3-319-15752-8

References[edit]