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Correlation=causation
X happens, then B happens. So, A caused B
Causation=correlation
The effect cause the cause
Confounding
Ignores another factor causing both
Overgeneralization
Small sample leads to broad conclusion
Anecdotal evidence
One case caused a universal rule
Composition
What’s true of the part of a sample is true of the whole sample
Scope shift
Evidence talks about X, conclusion talks about Y
Percentage v. Absolute numbers
Confusing as proportion with an actual amount
Temporal flaw
Past trend is assumed to continue unchanged
Circular reasoning
Conclusion just restate the premise
Equivocation
Word used in two different ways
Necessity v. Sufficiency confusion
Mistake is “required” for “enough”
Absence of evidence=evidence of absence
No proof so it must be false
Incorrect use of data
Misinterpreted stats/misapplied numbers
Comparison flaw
Assumes two things are comparable when they are not
Unwarranted assumption
Assumes something that is not supported at all
Loaded definition
Sneaks in a biased definition
False choice
Assumes only two options exist
Ad hominem
Attacks the person, not the argument
Straw man
Misrepresents the argument to attack it
Appeal to popularity/emotion/authority
Everyone believes it, trust me, emotional manipulation