Evaluating Logical Inferences: Strength, Fallacies, and Causal Connections

2. Chapter 9 Warranted Inferences

3. Learning Outcomes • Evaluate the logical strength of inferences presented to justify or support the belief that their conclusions are very probably, but not necessarily, true if we take their premises to be true • Recognize reasoning fallacies masquerading as warranted inferences

4. Chapter Opening Video

5. Evidence Currently at Hand • Weight of evidence • Evaluating generalizations • Coincidences, patterns, correlations, and causes

6. Weight of Evidence • Leads people towards a single conclusion • Logical strength of probabilistic arguments can be evaluated by systematic method for assigning levels of confidence

7. Evaluating Generalizations • Generalization is based on data gathered systematically or unsystematically • People over the age of 60 tend to prefer to listen to oldies • 73 percent of the hotel room beds in the city are infested with bedbugs

8. Evaluating Generalizations • Evaluation of logical strength of probabilistic generalizations • Requires asking questions and finding satisfactory answers • Was the correct group sampled? • Were the data obtained in an effective way? • Were enough cases considered? • Was the sample representatively structured?

9. Coincidences, Patterns, Correlations, and Causes • Coincidences • Events that occur together by chance • Patterns • Observed in events that initially appear to be random coincidences • Concentration of multi-million dollar luxury casinos in Las Vegas, Atlantic City

10. Coincidences, Patterns, Correlations, and Causes • Correlations • Describe the degree to which two different sets of events are aligned • Calculated using statistical analyses • Causes • Causal explanations are desirable as they enable us to explain, predict, and control parts of the natural world

11. Fallacies Masquerading as Warranted Arguments • Erroneous generalization • Playing with numbers • False dilemma • Gambler’s fallacy • False cause • Slippery slope

12. Erroneous Generalization • People make hasty and erroneous generalizations by: • Relying on little information • Exaggerating the importance of one or two particular experiences • Generalizations can be deceptively fallacious

13. Playing with Numbers • Arguments that use: • Raw numbers when percentages would present a fair-minded description • Percentages when raw numbers would present a fair-minded description

14. False Dilemma • Real dilemma - Situation in which all our choices are bad • At times, turns out to be a false dilemma on closer analysis • Referred to as the either/or fallacy

15. Gambler’s Fallacy • Random events that are not patterned, correlated, or causally connected • People make arguments with wrong assumption that what happens by chance is somehow connected with things we can control

16. False Cause • Assumption that two events are causally related as one happens after the other • Referred as post hoc, propter hoc

17. Discussion Questions • Give an example of when you connected some action you took with a positive result and then found yourself repeating it in the hope of producing a similar outcome • How did that associational inference work out for you? • Give reasons to support your conclusion

18. Slippery Slope • False assumption that events are linked together • First step in the process necessarily results in problems

19. Fallacies

20. Sketchnote Video