Past exam of the philosophy of science supplementary subject of the University of Oxford / 2018 / 6

Falsificationism gives an attractive account of one aspect of scientific progress: replace protected dogma by conjectures that make risky claims and expose them to serious tests. From $T\wedge A\Rightarrow E$ and an accepted $\neg E$, deduction rules out the conjunction. A theory that accommodates every possible outcome supplies less opportunity to learn from error. Karl Popper emphasizes exposure to refutation and warns against treating any convenient supporting instance as a demanding test.^[ref]

This captures why a successful novel prediction can be more informative than retrospectively adjusting a model to its original fitting data. It also helps articulate a progressive replacement: the new account should address the earlier successes and the discrepancy, while exposing itself to further independent tests. Merely adding a freely adjustable parameter after each failure need not improve knowledge.

The Duhem problem limits the simple account. A failed prediction is a failure of a package containing initial conditions, instrument assumptions and background theory. It is not a deductive identification of the particular target theory as false. Revising an auxiliary hypothesis can be rational when independently supported, but an unconstrained rescue can frustrate testing. Methodological judgement is needed to distinguish them; the bare logic of falsifiability does not do all the work.

There is also a positive-warrant problem. A theory's survival of past tests does not deductively entail that it will succeed tomorrow. Calling it corroborated records its demanding test history, but does not by itself solve the problem of induction. Nor does the elimination of one theory guarantee that a chosen rival is closer to the truth: many alternatives may remain, and progress can involve improved measurement, new concepts or increased explanatory scope as well as refutation.

A convincing form of falsificationism is therefore a critical methodology within a broader account. It explains the value of error detection, risky commitments and resistance to immunizing manoeuvres. It becomes less convincing as a complete logical algorithm for identifying falsified theories, ranking every successor or deriving confidence about future performance. A more adequate account combines severe criticism with independent checks on assumptions, comparative success and explicit acknowledgement of fallible inference.

Falsifiability is a relation between a claim, testing assumptions and possible outcomes that would conflict with it. It is not the fact that someone has criticized the claim, and not the same property as being false. It gives an important demand for empirical accountability: a theory that is compatible with every conceivable result cannot lose by an observational test. That demand motivates Popper's approach to the demarcation problem.^[ref]

Simply possessing a falsifiable hypothesis is nevertheless insufficient to make an entire activity scientific. A precise prediction can be false, based on unreliable methods, or repeatedly insulated from contrary evidence by new excuses. A practice that makes one testable prediction while disregarding all its failures does not acquire sound scientific methodology merely because a sentence in its vocabulary could have been refuted. How claims are tested and revised matters.

Strict deductive refutability is also a difficult necessary condition for every legitimate scientific claim. A stochastic radioactive decay model may assign nonzero probability to every finite sequence of counts. No individual finite record then logically contradicts it. For example, a Poisson distribution with $\lambda >0$ gives every integer count $k\ge 0$ probability $e^{-\lambda }{\lambda }^k/k!>0$. A sufficiently extreme count can nevertheless give strong evidence against the specified rate. A probability model can be tested through sampling distributions and error-controlled procedures. Extremely unlikely outcomes can count strongly against it without being impossible. Replacing scientific testing by a requirement of zero model probability would misdescribe much of quantitative science.

A further difficulty is the use of auxiliary hypotheses. A highly specific physical prediction becomes a test of the intended account only when instrument behaviour and background conditions are sufficiently controlled. Otherwise failure can be redirected indefinitely. Conversely, designing and calibrating an instrument can be genuine scientific work without itself taking the form of a single dramatic conjecture awaiting refutation.

The plausible conclusion is that falsifiability, or a suitably statistical form of testability, is one important component of a scientific practice. It does not provide a self-sufficient binary label for whole areas of inquiry. Publicly specified commitments, reliable methods, independent scrutiny and responsiveness to evidence are also needed. This preserves the force of the demand that claims risk failure without treating a useful methodological principle as a complete definition of science.