tools-only Logical Fallacies in Scientific Discourse
Fallacies of Causation
1. Post Hoc Ergo Propter Hoc (After This, Therefore Because of This)
Description: Assuming that because B happened after A, A caused B.
Examples:
- "I took this supplement and my cold went away, so the supplement cured my cold."
- "Autism diagnoses increased after vaccine schedules changed, so vaccines cause autism."
- "I wore my lucky socks and won the game, so the socks caused the win."
Why fallacious: Temporal sequence is necessary but not sufficient for causation. Correlation ≠ causation.
Related: Cum hoc ergo propter hoc (with this, therefore because of this) - correlation mistaken for causation even without temporal order.
2. Confusing Correlation with Causation
Description: Assuming correlation implies direct causal relationship.
Examples:
- "Countries that eat more chocolate have more Nobel Prize winners, so chocolate makes you smarter."
- "Ice cream sales correlate with drowning deaths, so ice cream causes drowning."
Reality: Often due to confounding variables (hot weather causes both ice cream sales and swimming).
3. Reverse Causation
Description: Confusing cause and effect direction.
Examples:
- "Depression is associated with inflammation, so inflammation causes depression." (Could be: depression causes inflammation)
- "Wealthy people are healthier, so wealth causes health." (Could be: health enables wealth accumulation)
Solution: Longitudinal studies and experimental designs to establish temporal order.
4. Single Cause Fallacy
Description: Attributing complex phenomena to one cause when multiple factors contribute.
Examples:
- "Crime is caused by poverty." (Ignores many other contributing factors)
- "Heart disease is caused by fat intake." (Oversimplifies multifactorial disease)
Reality: Most outcomes have multiple contributing causes.
Fallacies of Generalization
5. Hasty Generalization
Description: Drawing broad conclusions from insufficient evidence.
Examples:
- "My uncle smoked and lived to 90, so smoking isn't dangerous."
- "This drug worked in 5 patients, so it's effective for everyone."
- "I saw three black swans, so all swans are black."
Why fallacious: Small, unrepresentative samples don't support universal claims.
6. Anecdotal Fallacy
Description: Using personal experience or isolated examples as proof.
Examples:
- "I know someone who survived cancer using alternative medicine, so it works."
- "My grandmother never exercised and lived to 100, so exercise is unnecessary."
Why fallacious: Anecdotes are unreliable due to selection bias, memory bias, and confounding. Plural of anecdote ≠ data.
7. Cherry Picking (Suppressing Evidence)
Description: Selecting only evidence that supports your position while ignoring contradictory evidence.
Examples:
- Citing only studies showing supplement benefits while ignoring null findings
- Highlighting successful predictions while ignoring failed ones
- Showing graphs that start at convenient points
Detection: Look for systematic reviews, not individual studies.
8. Ecological Fallacy
Description: Inferring individual characteristics from group statistics.
Example:
- "Average income in this neighborhood is high, so this person must be wealthy."
- "This country has low disease rates, so any individual from there is unlikely to have disease."
Why fallacious: Group-level patterns don't necessarily apply to individuals.
Fallacies of Authority and Tradition
9. Appeal to Authority (Argumentum ad Verecundiam)
Description: Accepting claims because an authority figure said them, without evidence.
Examples:
- "Dr. X says this treatment works, so it must." (If Dr. X provides no data)
- "Einstein believed in God, so God exists." (Einstein's physics expertise doesn't transfer)
- "99% of doctors recommend..." (Appeal to majority + authority without evidence)
Valid use of authority: Experts providing evidence-based consensus in their domain.
Invalid: Authority opinions without evidence, or outside their expertise.
10. Appeal to Antiquity/Tradition
Description: Assuming something is true or good because it's old or traditional.
Examples:
- "Traditional medicine has been used for thousands of years, so it must work."
- "This theory has been accepted for decades, so it must be correct."
Why fallacious: Age doesn't determine validity. Many old beliefs have been disproven.
11. Appeal to Novelty
Description: Assuming something is better because it's new.
Examples:
- "This is the latest treatment, so it must be superior."
- "New research overturns everything we knew." (Often overstated)
Why fallacious: New ≠ better. Established treatments often outperform novel ones.
Fallacies of Relevance
12. Ad Hominem (Attack the Person)
Description: Attacking the person making the argument rather than the argument itself.
Types:
- Abusive: "He's an idiot, so his theory is wrong."
- Circumstantial: "She's funded by industry, so her findings are false."
- Tu Quoque: "You smoke, so your anti-smoking argument is invalid."
Why fallacious: Personal characteristics don't determine argument validity.
Note: Conflicts of interest are worth noting but don't invalidate evidence.
13. Genetic Fallacy
Description: Judging something based on its origin rather than its merits.
Examples:
- "This idea came from a drug company, so it's wrong."
- "Ancient Greeks believed this, so it's outdated."
Better approach: Evaluate evidence regardless of source.
14. Appeal to Emotion
Description: Manipulating emotions instead of presenting evidence.
Types:
- Appeal to fear: "If you don't vaccinate, your child will die."
- Appeal to pity: "Think of the suffering patients who need this unproven treatment."
- Appeal to flattery: "Smart people like you know that..."
Why fallacious: Emotional reactions don't determine truth.
15. Appeal to Consequences (Argumentum ad Consequentiam)
Description: Arguing something is true/false based on whether consequences are desirable.
Examples:
- "Climate change can't be real because the solutions would hurt the economy."
- "Free will must exist because without it, morality is impossible."
Why fallacious: Reality is independent of what we wish were true.
16. Appeal to Nature (Naturalistic Fallacy)
Description: Assuming "natural" means good, safe, or effective.
Examples:
- "This treatment is natural, so it's safe."
- "Organic food is natural, so it's healthier."
- "Vaccines are unnatural, so they're harmful."
Why fallacious:
- Many natural things are deadly (arsenic, snake venom, hurricanes)
- Many synthetic things are beneficial (antibiotics, vaccines)
- "Natural" is often poorly defined
17. Moralistic Fallacy
Description: Assuming what ought to be true is true.
Examples:
- "There shouldn't be sex differences in ability, so they don't exist."
- "People should be rational, so they are."
Why fallacious: Desires about reality don't change reality.
Fallacies of Structure
18. False Dichotomy (False Dilemma)
Description: Presenting only two options when more exist.
Examples:
- "Either you're with us or against us."
- "It's either genetic or environmental." (Usually both)
- "Either the treatment works or it doesn't." (Ignores partial effects)
Reality: Most issues have multiple options and shades of gray.
19. Begging the Question (Circular Reasoning)
Description: Assuming what you're trying to prove.
Examples:
- "This medicine works because it has healing properties." (What are healing properties? That it works!)
- "God exists because the Bible says so, and the Bible is true because it's God's word."
Detection: Check if the conclusion is hidden in the premises.
20. Moving the Goalposts
Description: Changing standards of evidence after initial standards are met.
Example:
- Skeptic: "Show me one study."
- [Shows study]
- Skeptic: "That's just one study; show me a meta-analysis."
- [Shows meta-analysis]
- Skeptic: "But meta-analyses have limitations..."
Why problematic: No amount of evidence will ever be sufficient.
21. Slippery Slope
Description: Arguing that one step will inevitably lead to extreme outcomes without justification.
Example:
- "If we allow gene editing for disease, we'll end up with designer babies and eugenics."
When valid: If intermediate steps are actually likely.
When fallacious: If chain of events is speculative without evidence.
22. Straw Man
Description: Misrepresenting an argument to make it easier to attack.
Example:
- Position: "We should teach evolution in schools."
- Straw man: "So you think we should tell kids they're just monkeys?"
Detection: Ask: Is this really what they're claiming?
Fallacies of Statistical and Scientific Reasoning
23. Texas Sharpshooter Fallacy
Description: Cherry-picking data clusters to fit a pattern, like shooting arrows then drawing targets around them.
Examples:
- Finding cancer clusters and claiming environmental causes (without accounting for random clustering)
- Data mining until finding significant correlations
Why fallacious: Patterns in random data are inevitable; finding them doesn't prove causation.
24. Base Rate Fallacy
Description: Ignoring prior probability when evaluating evidence.
Example:
- Disease affects 0.1% of population; test is 99% accurate
- Positive test ≠ 99% probability of disease
- Actually ~9% probability (due to false positives exceeding true positives)
Solution: Use Bayesian reasoning; consider base rates.
25. Prosecutor's Fallacy
Description: Confusing P(Evidence|Innocent) with P(Innocent|Evidence).
Example:
- "The probability of this DNA match occurring by chance is 1 in 1 million, so there's only a 1 in 1 million chance the defendant is innocent."
Why fallacious: Ignores base rates and prior probability.
26. McNamara Fallacy (Quantitative Fallacy)
Description: Focusing only on what can be easily measured while ignoring important unmeasured factors.
Example:
- Judging school quality only by test scores (ignoring creativity, social skills, ethics)
- Measuring healthcare only by quantifiable outcomes (ignoring quality of life)
Quote: "Not everything that counts can be counted, and not everything that can be counted counts."
27. Multiple Comparisons Fallacy
Description: Not accounting for increased false positive rate when testing many hypotheses.
Example:
- Testing 20 hypotheses at p < .05 gives ~65% chance of at least one false positive
- Claiming jellybean color X causes acne after testing 20 colors
Solution: Correct for multiple comparisons (Bonferroni, FDR).
28. Reification (Hypostatization)
Description: Treating abstract concepts as if they were concrete things.
Examples:
- "Evolution wants organisms to survive." (Evolution doesn't "want")
- "The gene for intelligence" (Intelligence isn't one gene)
- "Nature selects..." (Nature doesn't consciously select)
Why problematic: Can lead to confused thinking about mechanisms.
Fallacies of Scope and Definition
29. No True Scotsman
Description: Retroactively excluding counterexamples by redefining criteria.
Example:
- "No natural remedy has side effects."
- "But poison ivy is natural and causes reactions."
- "Well, no true natural remedy has side effects."
Why fallacious: Moves goalposts to protect claim from falsification.
30. Equivocation
Description: Using a word with multiple meanings inconsistently.
Example:
- "Evolution is just a theory. Theories are guesses. So evolution is just a guess."
- (Conflates colloquial "theory" with scientific "theory")
Detection: Check if key terms are used consistently.
31. Ambiguity
Description: Using vague language that can be interpreted multiple ways.
Example:
- "Quantum healing" (What does "quantum" mean here?)
- "Natural" (Animals? Not synthetic? Organic? Common?)
Why problematic: Claims become unfalsifiable when terms are undefined.
32. Mind Projection Fallacy
Description: Projecting mental constructs onto reality.
Example:
- Assuming categories that exist in language exist in nature
- "Which chromosome is the gene for X on?" when X is polygenic and partially environmental
Better: Recognize human categories may not carve nature at the joints.
Fallacies Specific to Science
33. Galileo Gambit
Description: "They laughed at Galileo, and he was right, so if they're laughing at me, I must be right too."
Why fallacious:
- They laughed at Galileo, and he was right
- They also laughed at countless crackpots who were wrong
- Being an outsider doesn't make you right
Reality: Revolutionary ideas are usually well-supported by evidence.
34. Argument from Ignorance (Ad Ignorantiam)
Description: Assuming something is true because it hasn't been proven false (or vice versa).
Examples:
- "No one has proven homeopathy doesn't work, so it works."
- "We haven't found evidence of harm, so it must be safe."
Why fallacious: Absence of evidence ≠ evidence of absence (though it can be, depending on how hard we've looked).
Burden of proof: Falls on the claimant, not the skeptic.
35. God of the Gaps
Description: Explaining gaps in knowledge by invoking supernatural or unfalsifiable causes.
Examples:
- "We don't fully understand consciousness, so it must be spiritual."
- "This complexity couldn't arise naturally, so it must be designed."
Why problematic:
- Fills gaps with non-explanations
- Discourages genuine investigation
- History shows gaps get filled by natural explanations
36. Nirvana Fallacy (Perfect Solution Fallacy)
Description: Rejecting solutions because they're imperfect.
Examples:
- "Vaccines aren't 100% effective, so they're worthless."
- "This diet doesn't work for everyone, so it doesn't work."
Reality: Most interventions are partial; perfection is rare.
Better: Compare to alternatives, not to perfection.
37. Special Pleading
Description: Applying standards to others but not to oneself.
Examples:
- "My anecdotes count as evidence, but yours don't."
- "Mainstream medicine needs RCTs, but my alternative doesn't."
- "Correlation doesn't imply causation—except when it supports my view."
Why fallacious: Evidence standards should apply consistently.
38. Unfalsifiability
Description: Formulating claims in ways that cannot be tested or disproven.
Examples:
- "This energy can't be detected by any instrument."
- "It works, but only if you truly believe."
- "Failures prove the conspiracy is even deeper."
Why problematic: Unfalsifiable claims aren't scientific; they can't be tested.
Good science: Makes specific, testable predictions.
39. Affirming the Consequent
Description: If A, then B. B is true. Therefore, A is true.
Example:
- "If the drug works, symptoms improve. Symptoms improved. Therefore, the drug worked."
- (Could be placebo, natural history, regression to mean)
Why fallacious: Other causes could produce the same outcome.
Valid form: Modus ponens: If A, then B. A is true. Therefore, B is true.
40. Denying the Antecedent
Description: If A, then B. A is false. Therefore, B is false.
Example:
- "If you have fever, you have infection. You don't have fever. Therefore, you don't have infection."
Why fallacious: B can be true even when A is false.
Avoiding Logical Fallacies
Practical Steps
Identify the claim - What exactly is being argued?
Identify the evidence - What supports the claim?
Check the logic - Does the evidence actually support the claim?
Look for hidden assumptions - What unstated beliefs does the argument rely on?
Consider alternatives - What other explanations fit the evidence?
Check for emotional manipulation - Is the argument relying on feelings rather than facts?
Evaluate the source - Are there conflicts of interest? Is this within their expertise?
Look for balance - Are counterarguments addressed fairly?
Assess the evidence - Is it anecdotal, observational, or experimental? How strong?
Be charitable - Interpret arguments in their strongest form (steel man, not straw man).
Questions to Ask
- Is the conclusion supported by the premises?
- Are there unstated assumptions?
- Is the evidence relevant to the conclusion?
- Are counterarguments acknowledged?
- Could alternative explanations account for the evidence?
- Is the reasoning consistent?
- Are terms defined clearly?
- Is evidence being cherry-picked?
- Are emotions being manipulated?
- Would this reasoning apply consistently to other cases?
Common Patterns
Good Arguments:
- Clearly defined terms
- Relevant, sufficient evidence
- Valid logical structure
- Acknowledges limitations and alternatives
- Proportional conclusions
- Transparent about uncertainty
- Applies consistent standards
Poor Arguments:
- Vague or shifting definitions
- Irrelevant or insufficient evidence
- Logical leaps
- Ignores counterevidence
- Overclaimed conclusions
- False certainty
- Double standards
Remember
- Fallacious reasoning doesn't mean the conclusion is false - just that this argument doesn't support it.
- Identifying fallacies isn't about winning - it's about better understanding reality.
- We all commit fallacies - recognizing them in ourselves is as important as in others.
- Charity principle - Interpret arguments generously; don't assume bad faith.
- Focus on claims, not people - Ad hominem goes both ways.