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Fallacy of composition

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A fallacy of composition arises when one infers that something is true of the whole from the fact that it is true of some (or even every) part of the whole. For example: "This fragment of metal cannot be broken with a hammer, therefore the machine of which it is a part cannot be broken with a hammer." This is clearly fallacious, because many machines can be broken into their constituent parts without any of those parts being so breakable.

This fallacy is often confused with the fallacy of hasty generalization, in which an unwarranted inference is made from a statement about a sample to a statement about the population from which it is drawn.

The fallacy of composition is the converse of the fallacy of division.

ApplicationEdit

In Keynesian macroeconomics, the "paradox of thrift" illustrates this fallacy: increasing saving (or "thrift") is obviously good for an individual, since it provides for retirement or a "rainy day," but if everyone saves more, it may cause a recession by reducing consumer demand. So here is one explicit argument (selected from a number of possibilities arising from these facts) that commits the fallacy of composition:

The thrift of any member of a group is beneficial to that member.
Therefore, the thrift of the group as a whole is beneficial to that group as a whole.

Commodity exports and dependency theory may also be used to demonstrate the fallacy of composition. Increases in the export of a certain commodity from one country may be beneficial to that country. However, exports of that commodity by many countries may flood the market, and thus drive down price, proving detrimental to the ensemble of exporting countries.

See alsoEdit

Informal fallacies
Special pleading | Red herring | Gambler's fallacy and its inverse
Fallacy of distribution (Composition | Division) | Begging the question | Many questions
Correlative-based fallacies:
False dilemma (Perfect solution) | Denying the correlative | Suppressed correlative
Deductive fallacies:
Accident | Converse accident
Inductive fallacies:
Hasty generalization | Overwhelming exception | Biased sample
False analogy | Misleading vividness | Conjunction fallacy
Vagueness:
False precision | Slippery slope
Ambiguity:
Amphibology | Continuum fallacy | False attribution (Contextomy | Quoting out of context)
Equivocation (Loki's Wager | No true Scotsman)
Questionable cause:
Correlation does not imply causation | Post hoc | Regression fallacy
Texas sharpshooter | Circular cause and consequence | Wrong direction | Single cause
Other types of fallacy
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