scientific-skills/scientific-critical-thinking/references/logical_fallacies.md
Description: Assuming that because B happened after A, A caused B.
Examples:
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:
Reality: Often due to confounding variables (hot weather causes both ice cream sales and swimming).
Description: Confusing cause and effect direction.
Examples:
Solution: Longitudinal studies and experimental designs to establish temporal order.
Description: Attributing complex phenomena to one cause when multiple factors contribute.
Examples:
Reality: Most outcomes have multiple contributing causes.
Description: Drawing broad conclusions from insufficient evidence.
Examples:
Why fallacious: Small, unrepresentative samples don't support universal claims.
Description: Using personal experience or isolated examples as proof.
Examples:
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:
Detection: Look for systematic reviews, not individual studies.
Description: Inferring individual characteristics from group statistics.
Example:
Why fallacious: Group-level patterns don't necessarily apply to individuals.
Description: Accepting claims because an authority figure said them, without evidence.
Examples:
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:
Why fallacious: Age doesn't determine validity. Many old beliefs have been disproven.
Description: Assuming something is better because it's new.
Examples:
Why fallacious: New ≠ better. Established treatments often outperform novel ones.
Description: Attacking the person making the argument rather than the argument itself.
Types:
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:
Better approach: Evaluate evidence regardless of source.
Description: Manipulating emotions instead of presenting evidence.
Types:
Why fallacious: Emotional reactions don't determine truth.
Description: Arguing something is true/false based on whether consequences are desirable.
Examples:
Why fallacious: Reality is independent of what we wish were true.
Description: Assuming "natural" means good, safe, or effective.
Examples:
Why fallacious:
Description: Assuming what ought to be true is true.
Examples:
Why fallacious: Desires about reality don't change reality.
Description: Presenting only two options when more exist.
Examples:
Reality: Most issues have multiple options and shades of gray.
Description: Assuming what you're trying to prove.
Examples:
Detection: Check if the conclusion is hidden in the premises.
Description: Changing standards of evidence after initial standards are met.
Example:
Why problematic: No amount of evidence will ever be sufficient.
Description: Arguing that one step will inevitably lead to extreme outcomes without justification.
Example:
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:
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:
Why fallacious: Patterns in random data are inevitable; finding them doesn't prove causation.
Description: Ignoring prior probability when evaluating evidence.
Example:
Solution: Use Bayesian reasoning; consider base rates.
Description: Confusing P(Evidence|Innocent) with P(Innocent|Evidence).
Example:
Why fallacious: Ignores base rates and prior probability.
Description: Focusing only on what can be easily measured while ignoring important unmeasured factors.
Example:
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:
Solution: Correct for multiple comparisons (Bonferroni, FDR).
Description: Treating abstract concepts as if they were concrete things.
Examples:
Why problematic: Can lead to confused thinking about mechanisms.
Description: Retroactively excluding counterexamples by redefining criteria.
Example:
Why fallacious: Moves goalposts to protect claim from falsification.
Description: Using a word with multiple meanings inconsistently.
Example:
Detection: Check if key terms are used consistently.
Description: Using vague language that can be interpreted multiple ways.
Example:
Why problematic: Claims become unfalsifiable when terms are undefined.
Description: Projecting mental constructs onto reality.
Example:
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:
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:
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:
Why problematic:
Description: Rejecting solutions because they're imperfect.
Examples:
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:
Why fallacious: Evidence standards should apply consistently.
Description: Formulating claims in ways that cannot be tested or disproven.
Examples:
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:
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:
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:
Poor Arguments: