# Logical Fallacies: The Complete List With Examples > Logical fallacies explained: 29 common fallacies grouped by type, each with a plain definition and an example, plus a summary table you can keep. Canonical: https://mentarium.io/critical-thinking/logical-fallacies.md Critical Thinking 9 min read By [Nemo](https://mentarium.io/about) Updated October 30, 2026 > **Logical fallacies** are errors in reasoning that make an argument look stronger than it is. Some attack the person instead of the point, some rely on ambiguous words, some assume what they should prove, and some misread causes or statistics. An argument with a fallacy can still have a true conclusion; it just hasn’t supported it. - **Category**: Critical thinking (reasoning errors) - **Origin**: Catalogued since Aristotle's Sophistical Refutations - **Best used for**: Reading the news, debating, reviewing proposals, checking your own arguments - **Related**: Critical thinking skills, cognitive biases, steelmanning ## 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](https://mentarium.io/critical-thinking/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](https://mentarium.io/mental-models/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](https://mentarium.io/biases/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.” The Mentarium Field Guide 60 mental models, organized by the decision in front of you Each model on one or two pages: when to use it, the questions to ask, an example and the trap. Plus the 25 biases that break decisions and 12 ready Model Stacks. [See Field Guide · $14](https://mentarium.io/shop/field-guide) ## 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](https://mentarium.io/biases/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](https://mentarium.io/mental-models/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](https://mentarium.io/biases/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: 1. **State the opposing view in a form its holders would accept** before you answer it. 2. **Separate the claim from the claimant.** Ask whether the argument would hold if someone you dislike made it. 3. **Ask “compared with what?”** for every number, and “what else could explain this?” for every cause. 4. **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](https://mentarium.io/biases/cognitive-biases-examples) page is a good companion. When the argument is about a real decision, the [decision-making hub](https://mentarium.io/decision-making) 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. ## FAQ ### 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. ## Related models & biases [Critical Thinking Critical Thinking Skills Critical thinking skills are the abilities that let you judge a claim on its merits: breaking an argument into parts, weighing evidence, drawing sound inferences, spotting fallacies and biases, considering alternatives and changing your mind when the evidence calls for it. They are learnable habits, not a fixed trait, and improve with deliberate practice.](https://mentarium.io/critical-thinking/critical-thinking-skills) [Cognitive bias 25 Cognitive Biases Examples From Everyday Life Cognitive biases are systematic patterns of error in how people think, judge and decide. They come from mental shortcuts that usually save time but go wrong in predictable ways. Examples include confirmation bias, anchoring, loss aversion and the sunk cost fallacy, and they affect everyone, regardless of intelligence.](https://mentarium.io/biases/cognitive-biases-examples) [Mental model Steelmanning Steelmanning is restating an opposing argument in its strongest, most charitable form before you respond to it. It is the opposite of strawmanning, which attacks a weakened version. A good steelman is one the other side would accept as fair, and it tests whether your own view survives the best case against it.](https://mentarium.io/mental-models/steelmanning) [Cognitive bias Survivorship Bias Survivorship bias is the error of drawing conclusions from the people, companies or things that made it through some selection process while ignoring the ones that did not. Because the failures are invisible, the survivors look like the whole story, and their traits get mistaken for the causes of success.](https://mentarium.io/biases/survivorship-bias) [Mental model Base Rates and Base Rate Neglect A base rate is how often something happens in a relevant population, before you look at any specific case. Base rate neglect (the base rate fallacy) is ignoring that number in favor of vivid case details. Kahneman and Tversky showed it with the taxicab problem, where most people overweight a witness and ignore how few cabs are Blue.](https://mentarium.io/mental-models/base-rates) Mentarium Decision Coach · free Use this with your AI Connect the free Decision Coach to Claude or ChatGPT and it walks you through the Decision Loop, picks the right Model Stack and runs the Bias Radar on your reasoning. [Connect it free](https://mentarium.io/coach) Mentarium Decision Coach · free Use this with your AI [Connect it free](https://mentarium.io/coach)