4 Fallacies of Weak Evidence

Fallacies of Weak Evidence

when an argument presents evidence that is too weak, distorted, or misused to logically support the conclusion

Hasty Generalization

draws a broad conclusion based on too small or unrepresentative a sample. It assumes the whole reflects the part without sufficient evidence

Weak (or False) Analogy

treats two things as logically similar when the comparison is flawed or superficial. It assumes relevant similarity where the actual connection is weak or irrelevant.

False Cause (Causal Fallacy)

assumes a causal relationship without sufficient proof that one thing actually caused the other. The events may be correlated or sequential, but causation is taken for granted.

False Cause - Post Hoc (“After this, therefore because of this”)

subtype assumes that because one event followed another, the first caused the second. It mistakes sequence for consequence

False Cause - Non Causa Pro Causa (“Not the cause for the cause”)

subtype names the wrong cause for an observed effect. It identifies something unrelated or coincidental as the explanation.

False Cause - Oversimplified Cause

subtype identifies a single cause for an outcome that is actually the result of multiple factors. It reduces complex situations to one variable.

Gambler’s Fallacy

This fallacy assumes that independent events in a random sequence are linked in probability. It falsely believes that past outcomes influence future ones when they do not

Appeal to Ignorance (when used as weak support)

argues that a claim is true because it has not been proven false, or false because it has not been proven true. It treats lack of evidence as positive evidence.

Appeal to Unqualified Authority

supports a conclusion by citing someone who lacks expertise in the relevant field. It assumes the person’s status makes their claim reliable. The source lacks the proper qualifications.

Misleading Statistics

presents numerical data in a way that misrepresents or distorts the truth. It may omit context, cherry-pick results, or use improper measurements. The numbers appear precise but give a false impression.

Base Rate Fallacy

ignores general statistical information (base rates) in favor of irrelevant specific details. It substitutes anecdotal or circumstantial data for reliable background probabilities. The argument downplays known likelihoods.

Texas Sharpshooter Fallacy

occurs when someone highlights patterns in data after the fact and treats them as significant. It selects only the points that fit a chosen conclusion and ignores the rest

Anthropic Fallacy

explains the existence of something by saying it must be that way because we are here to observe it. It treats observation as explanation. The fact that something exists is used to justify why it must exist