Thehypothetico-deductive model ormethod is a proposed description of thescientific method. According to it,scientific inquiry proceeds by formulating ahypothesis in 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]
One example of an algorithmic statement of the hypothetico-deductive method is as follows:[2]
One possible sequence in this model would be1,2,3,4. If the outcome of4 holds, and3 is not yet disproven, you may continue with3,4,1, and so forth; but if the outcome of4 shows3 to be false, you will have to go back to2 and 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])
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' 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 alsoconfirmation holism.[citation needed]
Qualification of corroborating evidence is sometimes raised as philosophically problematic. Theraven 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' islogically equivalent to 'all non-black things are non-ravens' (this is thecontrapositive 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'.[citation needed]
Attempted resolutions may distinguish:
Evidence contrary to a hypothesis is itself philosophically problematic. Such evidence is called afalsification of the hypothesis. However, under the theory ofconfirmation 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 suchimmunizing stratagems is conducive to the progress of science.[8]
PhysicistSean Carroll claims the model ignoresunderdetermination.[9]
The hypothetico-deductive approach contrasts with other research models such as theinductive 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.[10]
