Ch 8. Fallacies That Violate the Sufficiency Criterion

Arguments should provide relevant, acceptable, and sufficient evidence to justify their conclusions. The fallacies in this section fail to meet the sufficiency criterion by providing too little, biased, or misleading evidence, or by making unsupported causal claims.

1. Fallacies of Missing Evidence

These fallacies involve insufficient, omitted, or biased evidence, leading to weak or misleading conclusions.

2. Causal Fallacies

These fallacies involve incorrectly assuming or establishing causal relationships based on insufficient or flawed evidence.

Each of these fallacies fails to provide enough grounds for accepting the conclusion, violating the sufficiency criterion of a good argument.

Fallacies of Missing Evidence

These fallacies occur when an argument provides too little, biased, or misleading evidence, making the conclusion unjustified.

1. Insufficient Sample (Hasty Generalization)

Definition: Drawing a conclusion from too small a sample.
Example: “Vitamin C prevents colds because my family took it and didn’t get sick.”
Problem: A single family is not a sufficient sample to generalize for everyone.
Attack Strategy: Ask the arguer if they think a single case or small sample is enough to justify a broad conclusion.

2. Unrepresentative Data

Definition: Drawing a conclusion from biased or non-representative data.
Example: “43% of Americans spend 2+ hours in recreation daily, based on a Florida study.”
Problem: Florida’s population is not representative of all Americans.
Attack Strategy: Point out the sample’s lack of diversity and suggest alternative data sources.

3. Arguing from Ignorance

Definition: Claiming something is true simply because it has not been proven false (or vice versa).
Example: “Ghosts exist unless you can prove they don’t.”
Problem: The burden of proof is on the person making the claim.
Attack Strategy: Present the opposite claim using the same reasoning (e.g., “Ghosts don’t exist unless you can prove they do”).

4. Contrary-to-Fact Hypothesis

Definition: Claiming something would have happened differently if past events were changed, without sufficient evidence.
Example: “If I had practiced my backhand more, I would have won the tournament.”
Problem: There’s no way to know for sure.
Attack Strategy: Ask the arguer how they could possibly prove their claim.

5. Fallacy of Popular Wisdom

Definition: Using clichés or folk wisdom as if they were evidence.
Example: “It’s just common sense to pay off your mortgage instead of investing.”
Problem: “Common sense” is vague and unsupported.
Attack Strategy: Ask for actual evidence, or counter with a conflicting cliché.

6. Special Pleading

Definition: Applying a rule to others but making oneself an exception without justification.
Example: “I work hard, so I shouldn’t have to do chores, but my wife should.”
Problem: No valid reason is given for the exception.
Attack Strategy: Expose the double standard and ask for a justification.

7. Omission of Key Evidence

Definition: Leaving out crucial evidence needed to support the argument.
Example: “Let’s get married—we like the same things and love dogs!”
Problem: Missing key factors like love and long-term commitment.
Attack Strategy: Directly point out the missing evidence and ask if it affects the conclusion.

Causal Fallacies

These fallacies occur when an argument presents faulty causal reasoning, leading to incorrect conclusions about cause-and-effect relationships.

1. Confusion of a Necessary with a Sufficient Condition

Definition: Assuming that a necessary condition is also a sufficient condition.
Example: “This flashlight should work; I just put in new batteries.”
Problem: Batteries are necessary but not sufficient—other factors like wiring or the bulb also matter.
Attack Strategy: Clarify the difference between necessary and sufficient conditions using examples like practicing piano vs. becoming a concert pianist.

2. Causal Oversimplification

Definition: Attributing an event to a single cause when multiple factors are involved.
Example: “Kids today have no discipline because corporal punishment was banned.”
Problem: Many other factors contribute to discipline, such as parenting, social environment, and education.
Attack Strategy: Ask the arguer to consider additional possible causes and evaluate their significance.

3. Post Hoc Fallacy

Definition: Assuming that because event B follows event A, A must have caused B.
Example: “Ever since we stopped going to church, our business has suffered.”
Problem: Just because two events happen in sequence doesn’t mean one caused the other—correlation is not causation.
Attack Strategy: Use an absurd counterexample, such as “Every time I wear my lucky socks, my team wins.”

4. Confusion of Cause and Effect

Definition: Mistaking the effect for the cause.
Example: “Natalie gets good grades because she is the teacher’s pet.”
Problem: It’s more likely that Natalie is the teacher’s pet because she gets good grades.
Attack Strategy: Reverse the argument to see if the opposite makes more sense.

5. Neglect of a Common Cause

Definition: Failing to recognize that two related events may be caused by a third, underlying factor.
Example: “People who drink coffee are more likely to be stressed, so coffee causes stress.”
Problem: A third factor, like a demanding job, could be causing both.
Attack Strategy: Suggest a common cause that explains both events.

6. Domino Fallacy (Slippery Slope)

Definition: Assuming that one event will inevitably trigger a chain of related events leading to an extreme outcome.
Example: “If we allow gun control, next they’ll control how much food we buy, then how many kids we can have.”
Problem: There’s no proven causal connection between these events.
Attack Strategy: Ask the arguer for evidence of a causal link between each step, or counter with an equally absurd chain reaction.

7. Gambler’s Fallacy

Definition: Assuming that past random events affect future probabilities in independent chance situations.
Example: “I’ve lost five poker hands in a row, so I’m due for a win.”
Problem: Each hand is independent—the odds don’t change.
Attack Strategy: Use a counterexample like coin flips—even if a coin lands on heads 10 times, the next flip is still 50/50.