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Someone recently told me he felt that an entire scientific field's consensus is invalid because some of its experts had made incorrect conclusions in the past.

For example, "Scientists in the 1970's falsely concluded carbohydrates couldn't be converted into fat, therefore I'm going to ignore current research because nutrition scientists don't know what they're talking about."

Is there a name for this kind of logical fallacy, where a claim is made that one mistake completely discredits a group or discipline?

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  • It obviously depends, can you give more details? There are examples where a single mistake invalidates an argument or proof (see eg Wiles proof of Fermat's Last Theorem - he had a mistake in the originally published proof that required reworking his proof). Do you mean that the party in question at some point in the past was wrong about something, and therefore is claimed to be wrong about this particular thing? Or do you mean that it is a claimed a group is currently wrong about something, and therefore wrong about everything?
    – James Kingsbery
    Commented Apr 13, 2016 at 19:18
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    @JamesKingsbery a claimed a group was wrong about something in the past, and is therefore wrong about everything. Please see the example I added to the original question.
    – glenviewjeff
    Commented Apr 14, 2016 at 14:18

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Faulty generalization and genetic fallacy are certainly relevant, but I do not think they capture the most salient aspect pointed out in the OP. It is not just that a claim is being judged based on community's reputation, this is generally a case of genetic fallacy, but in practice we have no choice but to partly rely on the reputation of the claimant when we are not in a position to evaluate the claim independently (as is often the case with scientific claims). And faulty generalization does not quite fit because it is not that behavior of bad scientists is generalized to the rest, but rather that their performance unduly affects the evaluation of the whole community's output.

I believe the closest type is the semantic apex/nadir fallacy, which "occurs when a group's performance is evaluated using the performance of the example(s) doing best, not an unbiased and representative sample. Conversely, the nadir fallacy occurs when performance is evaluated using the example(s) doing worst". In more formal statistical contexts this is referred to as (negative) sampling bias because a "sample is collected in such a way that some members of the intended population are less likely to be included than others". More broadly, this is a specialized case of the fallacy of composition, which reduces properties of the whole to its parts.

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This seems to me a form of Genetic fallacy, in which the conclusion, e.g. of rejecting A's statement, is based on something relevant to A but not to their statement, e.g. on what A has done in the past. That is, in this fallacy one evaluates the maker(s) of the statement instead of the statement itself.

This is more general, of course, but I think it fits quite well.

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Faulty Generalization, I would say: https://en.wikipedia.org/wiki/Faulty_generalization

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