Strong inference

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]

Criticisms[edit]

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]

References[edit]