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 • Most experiments, in spite of careful experimental design, have at least some inherent ambiguity.

• Most hypotheses and their tests have associated assumptions and concepts. Refuting evidence indicates inadequacy of either the main hypothesis or corollaries, and one may not know confidently which to reject. Typically, the ‘hard core’ of a theory is relatively invulnerable to attack, and we refute or modify the ‘protective belt’ of ancillary hypotheses, assumptions, and conditions [Lakatos, 1970].

• Instead of directly testing a hypothesis, we usually test deductive or inductive predictions derived from the hypothesis. This prediction may be wrong rather than the hypothesis.

Proof or disproof of a hypothesis is often impossible; rarely, search for proof or disproof can be undesirable. Frequently the scientific community loses interest in endless tests of a hypothesis that is already judged to be quite successful; they change the focus to characterization of the phenomenon. Then inductive predictions are the target of experiments, because little ambiguity remains about whether one is testing the hypothesis or its inferred implications. Symbolically, if h is a hypothesis, pi is an inductive prediction, and pd is a deductive prediction, then some possible hypothesis tests are:
 * h, directly testable;
 * h⇒pd, pd testable so h testable;
 * h⇒ pi, pi testable but h is not directly tested.

A school of thought known as conventionalism recognizes the networked nature of most hypotheses and the associated ambiguity of most confirmation/refutation evidence, as well as the seductiveness of modifying an otherwise successful hypothesis to account for inconsistent observations. Conventionalists conclude that subjective judgment is required in evaluating hypotheses, and they suggest that values such as simplicity and scope are used in making these judgments.

If the conventionalists are correct about how science works, then the subjectivity of evidence evaluation is a major obstacle to our quest for reliable knowledge. The weakness of conventionalism is its fluidity. Two scientists can examine the same evidence and embrace opposing views, because of different criteria for evidence evaluation. Most hypotheses are wrong, but demonstration of their errors leads more often to a modification of the hypothesis than to its rejection. This band-aid approach, though powerful and often successful, can lead the researcher into evaluating how reasonable each slight modification is, without detecting how cumbersome and unreasonable the composite hypothesis has become. Unless one holds tightly to the criterion of simplicity, there is the danger that any wrong hypothesis will stay alive by cancerously becoming more and more bizarre and convoluted to account for each successive bit of inconsistent data. When Galileo aimed his telescope at the moon and described mountains and craters, his observations conflicted with Aristotelian cosmology, which claimed that all celestial objects are perfect spheres. A defender of the old view had this ad hoc explanation: an invisible, undetectable substance fills the craters and extends to the top of the mountains. Imre Lakatos [1970] attempted to put a brake on this unconstrained ad hoc hypothesis modification by imposing a standard: if a hypothesis is modified to account for a conflicting observation, then it must not only account for all previous results just as well as did the original hypothesis, but also make at least one new and successful prediction.