Common Verbal Fallacies

Flashcards for reviewing common verbal fallacies in clear thinking.

Argument (in Philosophy)

A set of statements where one statement, called the conclusion, is supported by other statements called premises, presenting reasons or evidence to persuade someone to believe a particular point of view or claim.

Overgeneralization

Drawing broad conclusions based on a single event, often using words like 'always' or 'never'.

Mental Filtering

Focusing solely on negative aspects of a situation while ignoring positive ones.

Personalization

Believing that negative events are directly your fault, even when they are not.

Jumping into conclusions

Making quick judgements without considering all the facts.

Mind reading

Assuming you know what someone else is thinking without any evidence.

Verbal Fallacy

A type of reasoning mistake in which language is confused or used wrongly. It might also be an argument with flawed( unsound) reasoning or extraneous points.

Fallacy of Equivocation

This fallacy is committed when a word is used first in one sense and then in another during the course of an argument allowing a conclusion which would not otherwise be possible.

Fallacy of Ambiguity

An ambiguity fallacy happens when an argument misleads by using confusing language or incorrect terminology, often exploiting multiple meanings of terms.

Fallacy of Division

Assuming that because something is true of a whole group, it must also be true of each individual part of that group.

Fallacy of Significance

The mistaken belief that a statistically significant result automatically translates to a practically meaningful or important effect, confusing statistical significance with real-world importance.

Fallacy of Many Questions (Complex Question)

A question posed, usually aggressively, which presupposes that the person asked has already answered questions that have not in fact been asked, combining two or more questions into one and demanding a 'yes' or 'no' answer.

Effect Size

Measures the practical magnitude of the observed difference, important to consider alongside statistical significance.