What is the difference between a logical fallacy and a cognitive bias?

A fallacy is a flaw in an argument: the reasons given don't support the conclusion. A cognitive bias is a flaw in the thinker: a systematic tilt in how we perceive or judge. Biases often produce fallacies, as when confirmation bias leads to cherry picking.

What are the most common logical fallacies?

In everyday arguments, the straw man, ad hominem, false dilemma, appeal to popularity, slippery slope and post hoc fallacy show up most. They're common because they're persuasive and easy to commit without noticing.

Does a fallacy mean the conclusion is false?

No. Assuming that is itself a fallacy, sometimes called the fallacy fallacy. A fallacy means the argument doesn't support its conclusion. The conclusion might still be true for other reasons.

What is the difference between formal and informal fallacies?

A formal fallacy is an error in the logical structure itself, like affirming the consequent. An informal fallacy is an error in content or context, like attacking the person. Most fallacies in everyday life are informal, and most on this page are too.

Should I point out fallacies in an argument?

Naming a fallacy rarely persuades anyone. It's more useful to show the gap directly: "That's a criticism of him, but what about the argument?" The real value of knowing fallacies is catching them in your own reasoning before you say it.

Logical Fallacies: Complete List With Examples

What are logical fallacies?

A logical fallacy is a mistake in reasoning that makes an argument seem convincing when its reasons don’t actually support its conclusion. Fallacies are persuasive because they mimic good arguments: they appeal to things that usually matter, like expertise, popularity or a sequence of events, in situations where they don’t.

People have catalogued them since Aristotle, and no list is truly complete; names and groupings vary between textbooks. This page covers 29 of the most useful, grouped into five families: relevance, ambiguity, presumption, causal and statistical. Examples are hypothetical. For the broader skill set, see critical thinking skills.

Fallacies of relevance

Fallacies of relevance offer reasons that have nothing to do with whether the conclusion is true. They distract from the point.

1. Ad hominem

Attacking the person making the argument instead of the argument.
Example: “Why listen to her views on the budget? She’s never run a business.”

2. Tu quoque (appeal to hypocrisy)

Dismissing a claim because the person making it doesn’t follow it.
Example: “My doctor says I should quit smoking, but he smokes.” His smoking doesn’t change the medical evidence.

3. Straw man

Misrepresenting someone’s position as a weaker one, then attacking that.
Example: “You want more bike lanes? So you want to ban cars.” The cure is steelmanning: answer the strongest version.

4. Red herring

Introducing an irrelevant topic to pull attention away from the issue.
Example: Asked about missed deadlines, a manager says, “Let’s not forget how hard everyone worked during the move.”

5. Appeal to authority

Treating a claim as true because an authority said it, especially outside their expertise or against the consensus of experts.
Example: “A famous actor says this diet cures fatigue, so it must.” Citing genuine expertise is fine; the fallacy is treating authority as proof.

6. Appeal to emotion

Using feelings such as fear, pity or pride in place of evidence.
Example: “Think of how you’d feel if something happened to your family. Buy the premium plan.”

7. Appeal to popularity (bandwagon)

Claiming something is true or good because many people believe or do it.
Example: “Millions of people use this supplement, so it works.” Related bias: the bandwagon effect.

8. Appeal to tradition

Claiming something is right because it has always been done that way.
Example: “We’ve always reviewed every expense by hand. Why change?”

9. Genetic fallacy

Judging a claim by its origin rather than its merits.
Example: “That idea came from a competitor, so it’s probably bad.”

Fallacies of ambiguity

Fallacies of ambiguity rely on words or structures that can mean more than one thing.

10. Equivocation

Using a word in two different senses within one argument.
Example: “A feather is light. What is light cannot be dark. So a feather cannot be dark.”

11. Amphiboly

Relying on a sentence whose grammar allows two readings.
Example: An ad says “Save up to 50% on selected items,” and most items are discounted by 5%.

12. Composition

Assuming what is true of the parts is true of the whole.
Example: “Every player on this team is a star, so the team will be great.”

13. Division

Assuming what is true of the whole is true of each part.
Example: “The company is profitable, so every one of its products makes money.”

14. Quoting out of context

Cutting a quote so it appears to say something it didn’t.
Example: A review reading “not a great film, but the music is great” is quoted on a poster as “great film.”

Fallacies of presumption

Fallacies of presumption smuggle in an assumption that hasn’t been proven.

15. Begging the question (circular reasoning)

Using the conclusion as one of the premises.
Example: “This source is trustworthy because it says it is reliable.”

16. False dilemma

Presenting two options as the only ones when others exist.
Example: “Either we launch next week or we lose the market.” There may be a smaller launch, a later date, or a different market.

17. Loaded question

Asking a question that assumes something unproven.
Example: “Have you stopped wasting the marketing budget?” Either answer admits waste.

18. No true Scotsman

Redefining a group to exclude counterexamples.
Example: “No real engineer writes code without tests.” “Lena does.” “Then she’s not a real engineer.”

19. Appeal to ignorance

Claiming something is true because it hasn’t been proven false, or false because it hasn’t been proven true.
Example: “Nobody has shown this app harms kids, so it’s safe.”

20. Moving the goalposts

Changing the standard of proof once the original one has been met.
Example: “Show me one study.” After seeing one: “One study proves nothing. Show me ten.”

Causal fallacies

Causal fallacies draw conclusions about cause and effect that the evidence doesn’t support.

21. Post hoc ergo propter hoc

Assuming that because B came after A, A caused B.
Example: “I wore my lucky shirt and we won. The shirt works.”

22. Correlation implies causation (cum hoc)

Assuming that two things that occur together must be causally linked. They may share a third cause, or the arrow may point the other way.
Example: “Cities with more firefighters have more fires, so firefighters cause fires.” Bigger cities have more of both.

23. Single cause (oversimplified cause)

Attributing an outcome with many causes to only one.
Example: “The product failed because of the bad logo.”

24. Slippery slope

Claiming one step will inevitably lead to an extreme result, without showing why each step follows.
Example: “If we let people work from home on Fridays, soon nobody will come in at all.”

Statistical fallacies

Statistical fallacies misuse numbers, samples or probability.

25. Hasty generalization

Drawing a broad conclusion from a small or unrepresentative sample.
Example: “Two customers complained about the price, so our pricing is wrong.”

26. Texas sharpshooter (cherry picking)

Selecting data that fits a conclusion after the fact and ignoring the rest. The name comes from a joke about a man who fires at a barn and paints the target around the tightest cluster of holes.
Example: A fund advertises only its three best years. Related bias: confirmation bias.

27. Base rate fallacy

Ignoring how common something is in general when judging a specific case.
Example: A test for a rare condition is described as “95% accurate,” so a positive result is assumed to mean a 95% chance of having it. If the condition is rare enough, most positives can still be false. More on base rates.

28. Gambler’s fallacy

Believing that past independent random events change the odds of the next one.
Example: “Red has come up six times in a row, so black is due.” Each spin of a fair wheel is independent.

29. Survivorship fallacy

Drawing conclusions only from the cases that made it through a filter, ignoring those that didn’t.
Example: “Dropouts become billionaires. Look at these founders.” The many dropouts who didn’t are invisible. See survivorship bias.

Logical fallacies summary table

#	Fallacy	Family	In one line
1	Ad hominem	Relevance	Attacks the person, not the point
2	Tu quoque	Relevance	“You do it too”
3	Straw man	Relevance	Attacks a weaker version
4	Red herring	Relevance	Changes the subject
5	Appeal to authority	Relevance	“An expert said so”
6	Appeal to emotion	Relevance	Feelings instead of evidence
7	Appeal to popularity	Relevance	“Everyone believes it”
8	Appeal to tradition	Relevance	“It’s always been this way”
9	Genetic fallacy	Relevance	Judges by origin
10	Equivocation	Ambiguity	One word, two meanings
11	Amphiboly	Ambiguity	Ambiguous grammar
12	Composition	Ambiguity	Parts to whole
13	Division	Ambiguity	Whole to parts
14	Quoting out of context	Ambiguity	Selective quotation
15	Begging the question	Presumption	Conclusion as premise
16	False dilemma	Presumption	Only two options offered
17	Loaded question	Presumption	Question with a hidden claim
18	No true Scotsman	Presumption	Redefines to dodge counterexamples
19	Appeal to ignorance	Presumption	“Not disproven, so true”
20	Moving the goalposts	Presumption	Raises the bar after it’s met
21	Post hoc	Causal	After, therefore because of
22	Correlation as causation	Causal	Together, therefore linked
23	Single cause	Causal	One cause for many
24	Slippery slope	Causal	One step leads to disaster
25	Hasty generalization	Statistical	Too small a sample
26	Texas sharpshooter	Statistical	Cherry-picked data
27	Base rate fallacy	Statistical	Ignores how common it is
28	Gambler’s fallacy	Statistical	“It’s due”
29	Survivorship fallacy	Statistical	Only the winners counted

How to avoid logical fallacies

You avoid fallacies mostly by slowing down at the moment you feel most convinced. A few habits help:

State the opposing view in a form its holders would accept before you answer it.
Separate the claim from the claimant. Ask whether the argument would hold if someone you dislike made it.
Ask “compared with what?” for every number, and “what else could explain this?” for every cause.
Look for the missing cases: the failures, the silent customers, the years not shown.

Many fallacies are cognitive biases dressed up as arguments, so the cognitive biases examples page is a good companion. When the argument is about a real decision, the decision-making hub shows how to test it before you commit.

The Mentarium take

Learning fallacy names is useful for about a week, then it becomes a trap. People start winning arguments by labeling (“that’s a slippery slope!”) instead of engaging, and some slopes really are slippery. Every fallacy on this list has a reasonable cousin: experts are usually worth listening to, popular things are sometimes popular because they work, and A often does cause B.

The real question is never “does this argument contain a fallacy?” It’s “do these reasons support this conclusion, and how strongly?”

Our suggestion: pick the five fallacies you personally commit most. For most people that’s the straw man, the false dilemma, hasty generalization, post hoc and cherry picking. Then use this list as a mirror, not a weapon. The arguments most worth checking are the ones you’re about to make.