Logical Fallacies

1. Logical Fallacies Introduction

2. What is a logical fallacy? • A fallacy is an error of reasoning. These are flawed statements that often sound true • Logical fallacies are often used to strengthen an argument, but if the reader detects them the argument can backfire, and damage the writer’s credibility

3. Why study logical fallacies? • It is important to develop logical fallacy detection skills in your own writing, as well as others’. Think of this as “intellectual kung-fu: the art of intellectual self defense.” (Logical Fallacies Handlist)

4. Types of Logical Fallacies The following slides will briefly explain 15 fallacious arguments.

5. Types of Logical Fallacies • 1. “Bandwagon Approach” • - All Republicans MUST support the Republican President (or all Democrats MUST hate the Republican President) - ”1500 innocent children ripped from their mothers’ arms at our border. Lost in Trump’s “system”. Give us your tired, your poor, your huddle masses yearning to breathe free — and we will torture them for wanting a better life. From Shining City to Evil Empire in under 500 days.” — tweet Sunday by actor Jim Carrey.

6. .“Slippery Slope” • A fallacious argument built on the supposition that a small step will lead to a larger chain of events “If you don’t stop smoking, then you are going to start shooting heroin.” • “Marijuana is the gateway drug.”

7. Slippery Slope, cont.

8. Appeals to Authority • Using a celebrity or authority figure’s name to support an issue which if not really his or her expertise. • “Katie Perry thinks the killer should be pardoned.” FALSE AUTHORITY . • Often called.. . Using a suspicious, incredible source to defend a conclusion

9. Hasty Generalizations • This is the chief error in reasoning! – a type of inductive reasoning – conclusions made through insufficient evidence Ex: Two black men broke into a house. Any black man seen near a house is about to break into it. - Some immigrants are trying to come in the US illegally. All immigrants from the South are criminals.

10. Begging the question A is true because A is true . . . “chocolate is healthy because it is good for you” You know what they said, you saw it and the leaks are absolutely real. The news is fake because so much of the news is fake. [Trump, 2/16/17]

11. Straw Man • Misrepresent an argument to make it easier to attack (Ex: How could he possibly cheat on his taxes? He’s such a great father!)

12. Straw Man, con.

13. Appeals to Pity • Appeals to Pity: appealing to someone’s emotions in order to distract them from the truth.

14. Ad Hominem • Latin for “To the man” – pointing out a negative characteristic to support one’s argument. • I just realized that if you listen to Carly Fiorina for more than ten minutes straight, you develop a massive headache. She has zero chance! • Wacky @NYTimesDowd, who hardly knows me, makes up things that I never said for her boring interviews and column. A neurotic dope!

15. Non sequitur • Conclusion doesn’t logically follow the premise (Men are human. Mary is human. Therefore, Mary is a man.) • A caravan is headed towards the US. Criminals are in Caravans. Criminals are Invading the US

16. Cherry picking • Cherry picking is also extremely common in politics. A lot of politicians use data that makes them look better like an improved economy or increased trade. This is cherry picking when the data doesn’t show the full picture, for example if the politician has manipulated the start and end dates to eliminate inconvenient data points, or if they are leaving out important context. In the 2004 election, George Bush claimed that “Kerry’s plan will raise taxes by at least $900 billion his first hundred days.” Though people found that Kerry’s health plan would cost $895 billion, they also found that after including cost-cutting measures, it would actually cost $653 billion. When listening to statistics from politicians, look for the context of the data.

17. 14) Either/Or fallacy – aka False Dilemma . . . “You’re either part of the solution or part of the problem”

18. 15.) Post Hoc • The arguer infers that because one event follows another, the first event must be the cause of the second event. • Causation without correlation • Ex: The rooster crowed, the sun came up. Therefore, the rooster made the sun come up.