Past exam of the philosophy of science supplementary subject of the University of Oxford / 2019 / 5

There are useful distinctions between science and pseudoscience, but one simple property of an isolated sentence is unlikely to classify every historical practice correctly. The relevant assessment should examine both empirical commitments and how a programme responds to evidence.

Falsifiability is a valuable starting point. A claim that excludes no possible observational outcome cannot receive the same kind of empirical support as one that risks failure. A prediction made before an experiment, with conditions and tolerances specified, is more informative than a story adjusted afterwards to fit any result. This distinguishes taking an empirical risk from merely finding a favourable description of what happened.^[ref]

However, falsifiability is neither sufficient for being good science nor identical to having evidence. An arbitrary precise prediction can be falsifiable and poorly motivated. A well-supported theory can also encounter an apparent failure caused by an auxiliary hypothesis about calibration or initial conditions. The Duhem problem explains why sensible investigators sometimes repair an apparatus model rather than immediately discard a theory. It does not justify repairs chosen solely to prevent every conceivable adverse result.

A more informative comparison asks whether revisions produce independently testable predictions, whether controls address plausible errors, whether unfavourable results are recorded and whether others can reproduce the procedures. An adjustment can be scientifically productive if it connects a former discrepancy with new phenomena. The same algebraic adjustment is much less impressive if it does nothing beyond reproducing the data used to choose it. Scientific reproducibility helps make a research practice corrigible, although reproducibility alone cannot establish that its interpretation is correct.

Historical judgement also requires separating an immature proposal from a practice organized to evade criticism. A new mechanism may initially lack a precise experimental test. Its current weakness is a reason to qualify confidence, not proof that no further development can make it scientific. Conversely, technical vocabulary and numerical output do not make a programme scientific when its central claims remain insulated from evidence.

The demarcation should therefore be explained through concrete features of inquiry: identifiable hypotheses, discriminating tests, calibration, treatment of alternatives and accountable revision. Different features can matter differently across observational, experimental and historical sciences. Borderline cases do not erase clear failures, just as the absence of a sharp temperature boundary between warm and cold does not make all temperatures equal.

This approach also keeps the conclusion proportionate. Calling a practice scientific does not guarantee truth, ethical acceptability or practical usefulness. Calling it pseudoscientific should identify the evidential or methodological failure, rather than replace the argument with a label.

Falsificationism captures an important asymmetry between universal claims and counterexamples, but it is an incomplete account of scientific appraisal. It works best as a demand for serious testing within fallible theoretical packages, rather than as a mechanical rule for discarding a theory after any anomalous observation.

For an unrestricted universal claim, finitely many successful cases do not deductively establish every unobserved instance. One genuine contrary case can contradict the universal claim, provided the predicates and circumstances are correctly identified. Karl Popper's emphasis on attempts at refutation consequently highlights something that mere accumulation of agreeable examples can miss: a test is informative partly because plausible alternative outcomes would have counted against the proposal.^[ref]

Real predictions nevertheless involve a conjunction. If $T\wedge A\Rightarrow E$ and $\neg E$, thenHere $A$ includes an apparatus model and initial conditions. The conclusion does not identify $T$ as the failed component. Independent calibration, varied experimental arrangements and agreement across instruments help distinguish the possibilities; they do not eliminate the logical structure of the Duhem problem. Insisting that every auxiliary assumption remain unrevisable would obstruct ordinary experimental repair.

Probabilistic theories create another difficulty for simple deductive falsification. If an ideal Bernoulli distribution model assigns a probability $0<q<1$ to success, every finite binary sequence has positive probability. An unusual sequence does not literally contradict the model. A statistical hypothesis test can instead use a predeclared rejection region with a controlled type I error. Its possible mistaken rejection is part of the method, not a contradiction to be ignored. Comparing alternatives also requires likelihoods or other explicit criteria, rather than a binary division into refuted and unrefuted proposals.

Finally, survival alone is not enough. A vague conjecture and a quantitatively successful theory might both be unrefuted; they need not be equally supported. Scientific decisions also concern approximation error, independent explanatory connections and whether a proposed modification predicts anything new. These considerations need not restore conclusive verification, but they require more than deductive refutation.

The plausible core is therefore methodological: specify what could count against a claim, expose it to demanding tests and resist changes made only to accommodate failure. The stronger thesis that this principle supplies a complete logic of discovery, theory choice and confirmation is not established. Critical testing remains valuable precisely when its assumptions and statistical limitations are stated honestly.