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Statistical syllogisms

Statistical syllogisms. ...and why generalizations aren’t always accurate. What is a statisical syllogism?. Definition. type of inductive reasoning based on a probability where the strength of the argument is reliant on the strength of a generalization (major premise).

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Statistical syllogisms

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  1. Statistical syllogisms ...and why generalizations aren’t always accurate

  2. What is a statisicalsyllogism?

  3. Definition type of inductive reasoning based on a probability where the strength of the argument is reliant on the strength of a generalization (major premise)

  4. WHAT COMPOSES a Statistical Syllogism?

  5. MAJOR PREMISE generalizations which state probabilities that form the basisof succeeding assumptions

  6. Minor Premise statement that links the subject/s of the conclusion with the major premise

  7. CONCLUSION The assumption made based on the major premise.

  8. Major Premise 82.5% of IMed students are from PSHS.

  9. Minor premise Jon is an IMed student.

  10. Conclusion Jon is a most probably a graduate of PSHS.

  11. Major Premise 17.5% of IMed students are members of the Med. Choir.

  12. Minor Premise Flo is an IMed student.

  13. Conclusion It is very likely that Flo is not a member of the Med. Choir.

  14. Evaluatingthe strength of this type of argument is a matter of degree.

  15. The reliability of the argument must be evaluated using three questions.

  16. Are there enough cases to support a universal statement or one that is merely general?

  17. Have the observed cases been found in every variety of times, places and circumstances?

  18. Has a thorough search been made for conflicting cases?

  19. criteria for evaluating the strength of a generalization

  20. The closer the number of the sample to the required number, the more reliable the generalization is. Ex. Most apples are red. (If 100 apples exist in the world, the sample must approach 50 in order to be considered reliable.)

  21. The greater the variety of the members of the sample, the more reliable the generalization is. Ex. 75% of Asians are shorter than 5’11”. (The statement would be more reliable if the sample included a greater variety of Asians instead of just one nationality.)

  22. The more thorough the search for conflicting cases, the more reliable the generalization. Ex. 90% of men like chocolates. (If the number of conflicting cases increases in the sample taken, the generalization is made less reliable.)

  23. Fallaciesinvolving statisticalsyllogism

  24. accident application of a general rule when circumstancessuggest an exception.

  25. Converse accident application of an exception to the rule when the generalization should apply.

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