V. Maximizing Falsifiability

This insight, that science will progress faster if it focuses on differentiating evidence, is extremely profound, and the most important concept in the philosophy of science. It is known to many as “Falsificationism” and associated with Sir Karl Popper, who argued that good scientific practice is defined by presenting theories alongside criteria by which they can be rejected, so that all effort can be focused on trying to refute them by testing the potentially falsifying predictions derived from them. A strong theory is a theory that points towards its own Achilles heel, and because (according to Popper) for Freud and Marx there are no conceivable “ugly facts” that could show them to be incorrect, they are closed to criticism and worthless as predictors.

Science is like an optical fibre - some kinds of feedback allow it to progress faster than others

Science essentially serves to gauge the predictive power of a theory. According to “instrumentalism”, this is all there is to it – theories are nothing but means of making predictions. A relatively good theory therefore is one that:

  • Is precise in its predictions, so as to forbid relatively more outcomes, thus providing more scenarios by which it can be rejected. For example, if you have two dots on a Cartesian plane, the hypothesis “The relationship is linear” is easier to falsify (just test a new dot not aligned with the two others!) than “The relationship is quadratic”, because the third dot is consistent with more quadratic data patterns. A vaguer theory is less strengthened (“corroborated”) by confirmatory data than a more specific one, because its Bayesian likelihood P(data|hypothesis) is lower.
  • Makes predictions across a wide range of domains. For example, psychological theories are much less precise than physical ones, and the hypotheses derived from are typically a modest statement like “What condition a participant is under will make a difference”, without specifying the size or direction of this difference. However, psychological theories can often give rise to a wider variety of predictions, like how the theory about confirmation bias may partly predict both depression and paranoia.

The two criteria may appear to be contradictory, but they are really just about grain and extent, the same old filter-dimensions we have spoken of before. A good theory is fine-grained and wide-eyed.

The more data patterns a theory excludes, and the more diverse range of domains it makes predictions for, the more falsifiable it is.

Moreover, in order to truly gauge its value how well it predicts, as opposed to how accurately it fitswith evidence, it matters whether the theory is stated given the evidence (“post hoc”) or in ignorance of the evidence (“a priori” – typically, but not necessarily, before the evidence is gathered). A parallel distinction is often drawn between “data fitting”, such as finding a mathematical function that approximates a given dataset, and “ex ante prediction”, where the function is stated in advance. The reason for why this is important is because with post hoc explanations it is impossible to know how much of the data is influenced by random factors – noise – and if the explanation takes these into account, it will fare worse as a predictor, since in future circumstances, the noise will be different. If a prediction fails, the theory must be modified, according to Popper by suggesting new tests, so as to increase its falsifiability.

In evolution, if the ecological structures changes for a species, an adaptation may no longer be beneficial, no matter how useful it has been in the past. There is a similar concept called the “problem of induction”, associated with David Hume, that states that any empirically based generalizations (such as “all swans are white”) are vulnerable to refutation simply by counter-example (finding a black swan). With such a disquieting asymmetry, a theory would be hard to build but easy to dismantle. Popper too argued that how well a theory has generalized in the past does not matter for its correctness, and if it ever fails, it is no longer serviceable.

The interpretation of data depends on a basis of other, often more corroborated theories. Therefore, a theory's track record matters for how easy it is to falsify.

The interpretation of data depends on a basis of other, often more corroborated theories. Therefore, a theory's track record matters for how easy it is to falsify.

However, falsifying evidence should not be accepted without caution. Philosopher Pierre Duham has pointed out that a theory cannot be completely rejected, since the apparatus or the background assumptions could have caused the result. When in 2011 physicists at CERN detected neutrinos travelling at a speed faster than light, they did not immediately declare Einstein’s special relativity theory false, but instead –based on the theory’s previous track record – tenaciously sought after sources of experimental error (which they eventually found). The interpretation of any hypothesis presupposes facts that themselves are conjectural in nature, causing a web-like regress of uncertainty, but, Bayesian reasoning goes, pre-existing knowledge does and should matter in how we update our beliefs.