Editing Hypothetico-deductive model (section)

==Discussion==
Additionally, as pointed out by [[Carl 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.<ref>Murzi, Mauro (2001, 2008), "[http://www.iep.utm.edu/h/hempel.htm Carl Gustav Hempel (1905—1997)]", ''Internet Encyclopedia of Philosophy''. Murzi used the term relative frequency rather than probability.</ref>  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 a [[Bayesian analysis]], with each experimental result shifting the probability either up or down. [[Bayes' theorem]] shows 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 also [[confirmation holism]].{{cn|date=March 2025}}

Qualification of corroborating evidence is sometimes raised as philosophically problematic. The [[raven paradox]] is 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' is [[Logical equivalence|logically equivalent]] to 'all non-black things are non-ravens' (this is the [[contraposition|contrapositive]] form 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'. {{cn|date=March 2025}} 

Attempted resolutions may distinguish:
* non-falsifying observations as to strong, moderate, or weak corroborations{{cn|date=March 2025}}
* investigations that do or do not provide a potentially falsifying test of the hypothesis.<ref>[[John W. N. Watkins]] (1984), ''Science and Skepticism'', p. 319.</ref>

Evidence contrary to a hypothesis is itself philosophically problematic. Such evidence is called a [[falsifiability|falsification]] of the hypothesis. However, under the theory of [[confirmation holism]] it 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 Popper]] acknowledged this but maintained that a critical approach respecting methodological rules that avoided such ''immunizing stratagems'' is conducive to the progress of science.<ref>Karl R. Popper (1979, Rev. ed.), ''Objective Knowledge'', pp. 30, 360.
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Physicist [[Sean M. Carroll|Sean Carroll]] claims the model ignores [[underdetermination]].<ref>{{Cite web |url=http://www.preposterousuniverse.com/blog/2013/07/03/what-is-science/ |author=Sean Carroll |title=What is Science?|date=3 July 2013 }}</ref>

===Versus other research models===

The hypothetico-deductive approach contrasts with other research models such as the [[Inductivism|inductive approach]] or 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. <ref>{{Citation |last= Mesly |first= Olivier | date= 2015 |title= Creating Models in Psychological Research | location= United States | publisher= Springer Psychology  |pages= 126  |isbn = 978-3-319-15752-8}}
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