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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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?
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.
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.
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.
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."
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).
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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).
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