gh-k-dense-ai-claude-scientific-writer/skills/scientific-critical-thinking/references/logical_fallacies.md

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

1. Identify the claim - What exactly is being argued?

2. Identify the evidence - What supports the claim?

3. Check the logic - Does the evidence actually support the claim?

4. Look for hidden assumptions - What unstated beliefs does the argument rely on?

5. Consider alternatives - What other explanations fit the evidence?

6. Check for emotional manipulation - Is the argument relying on feelings rather than facts?

7. Evaluate the source - Are there conflicts of interest? Is this within their expertise?

8. Look for balance - Are counterarguments addressed fairly?

9. Assess the evidence - Is it anecdotal, observational, or experimental? How strong?

10. 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.