Strong inference

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In philosophy of science, strong inference is a model of scientific inquiry that emphasizes the need for alternative hypotheses, rather than a single hypothesis to avoid confirmation bias.

The term "strong inference" was coined by John R. Platt,[1] a biophysicist at the University of Chicago. Platt notes that some fields, such as molecular biology and high-energy physics, seem to adhere strongly to strong inference, with very beneficial results for the rate of progress in those fields.

The single hypothesis problem[edit]

The problem with single hypotheses, confirmation bias, was aptly described by Thomas Chrowder Chamberlin in 1897:

The moment one has offered an original explanation for a phenomenon which seems satisfactory, that moment affection for [one’s] intellectual child springs into existence, and as the explanation grows into a definite theory [one’s] parental affections cluster about [the] offspring and it grows more and more dear .... There springs up also unwittingly a pressing of the theory to make it fit the facts and a pressing of the facts to make them fit the theory... The temptation to misinterpret results that contradict the desired hypothesis is probably irresistible.[2]

Despite the admonitions of Platt, reviewers of grant-applications often require "A Hypothesis" as part of the proposal (note the singular). Peer-review of research can help avoid the mistakes of single-hypotheses, but only so long as the reviewers are not in the thrall of the same hypothesis. If there is a shared enthrallment among the reviewers in a commonly believed hypothesis, then innovation becomes difficult because alternative hypotheses are not seriously considered, and sometimes not even permitted.

Strong Inference[edit]

The method, very similar to the scientific method, is described as:

  1. Devising alternative hypotheses;
  2. Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses;
  3. Carrying out the experiment(s) so as to get a clean result;
  4. Recycling the procedure, making subhypotheses or sequential hypotheses to refine the possibilities that remain, and so on.

The methods of Grey system theory effectively entertain strong inference.[3][4] In such methods, the first step is the nullification of the single hypothesis by assuming that the true information of the system under study is only partially known.[5]


The original paper outlining strong inference has been criticized, particularly for overstating the degree that certain fields used this method.[6][7]

Strong inference plus[edit]

The limitations of Strong-Inference can be corrected by having two preceding phases:[2]

  1. An exploratory phase: at this point information is inadequate so observations are chosen randomly or intuitively or based on scientific creativity.
  2. A pilot phase: in this phase statistical power is determined by replicating experiments under identical experimental conditions.

These phases create the critical seed observation (s) upon which one can base alternative hypotheses.[2]


  1. ^ John R. Platt (1964). "Strong inference". Science. 146 (3642): 347–53. Bibcode:1964Sci...146..347P. doi:10.1126/science.146.3642.347. PMID 17739513.
  2. ^ a b c Don L. Jewett (1 January 2005). "What's wrong with single hypotheses? Why it is time for Strong-Inference-PLUS". Scientist (Philadelphia, Pa.). 19 (21): 10. PMC 2048741. PMID 17975652.
  3. ^ Haken, Hermann (28 January 2011). "Grey Information: Theory and Practical Applications". Grey Systems: Theory and Application. 1 (1): 105–106. doi:10.1108/gs.2011. ISSN 2043-9377.
  4. ^ Ju-Long, Deng (March 1982). "Control problems of grey systems". Systems & Control Letters. 1 (5): 288–294. doi:10.1016/S0167-6911(82)80025-X.
  5. ^ Javed, Saad Ahmed; Mahmoudi, Amin; Liu, Sifeng (June 2020). "Grey Absolute Decision Analysis (GADA) Method for Multiple Criteria Group Decision-Making Under Uncertainty". International Journal of Fuzzy Systems. 22 (4): 1073–1090. doi:10.1007/s40815-020-00827-8. ISSN 1562-2479. S2CID 256426658.
  6. ^ O'Donohue, William; Buchanan, Jeffrey A. (2001). "The weaknesses of strong inference". Behavior and Philosophy. 29. Cambridge Center for Behavioral Studies: 1–20.
  7. ^ Rowland H. Davis (2006). "Strong Inference: rationale or inspiration?". Perspectives in Biology and Medicine. 49 (2): 238–250. doi:10.1353/pbm.2006.0022. PMID 16702707. S2CID 42507520.