Populations and samples

1. Populations and samples Review of Statement Types

2. Question from reading • Random sample of 834 Florida residents • 54% of sample favor handgun control • What can we conclude about the population estimate? • The NRA is not very effective in Florida. • There is too much uncertainty to make any claim. • 54% support handgun control. • Inference to population is 51%-57% favoring handgun control.

3. Question from reading • Sample of 6000 CNN website readers • 65% of sample report they want to vote for Romney • What is the population estimate? • Republicans are going to win big states, making it more likely that Romney will win the presidency. • There is too much uncertainty to make any claim. • We can infer that the other 35% support Obama. • Inference to population is 61%-69%.

4. Practice statements • Infer which kind of argument from the type of evidence used to make that argument • Normative (what should be) • Interpretive (text) • Empirical (verifiable)

5. Practice statements • Infer which kind of argument from the type of evidence used to make that argument • Causal – • falsifiable • not provable • not a single incident (he caused the car wreck) • causal connection between two concepts • A driver’s BAC levels cause an increased number of accidents

6. Causality: BAC and accidents

7. Causality Political Tolerance and Education 6 Slope 5 4 Political Tolerance Mean 3 2 1 0 0 1 2 3 4 5 6 Education

8. Units of analysis • People • Places (cities, countries) • Time (quarters, years)

9. Causality • Something that varies across some units causes something else that also varies across the same units • Can be measured quantitatively (even if this is measured as low, medium, high)

10. Causal arguments? • The liberalism of the Supreme Court caused the health care decision. • Nominating residential ideology affects the ideology of Supreme Court justices. • Levels of saturated fat in the diet causes an increased probability of getting heart disease. • Cancer caused John’s death.

11. Normative … …statements usually are statements of opinion that give clues about the speaker’s personal value biases. They often have the word “should” in them. Remember if there is any element of normative bias in the statement, then it is normative. If it requires the assumption that the reader agrees with the values of the author, then it is normative.

12. Interpretive … … statements are when people are interpreting a text. The text could be a Constitution, the Bible or a work of political philosophy. People can interpret texts in certain ways that may emanate from their biases, but nonetheless could be a valid interpretation. Interpretive statements are different from empirical statements because interpretive statements must be falsifiable – in other words, they must be able to be wrong.

13. Empirical … …statements are 100% verifiable. They are facts. Factual statements may not be true. But it would be 100% verifiable to find out whether they are true or not. The difference between empirical statements and causal statements is that factual statements are verifiable events. They are either 100% true or 100% false. At times, the difference between the two will be a judgment call based on how much agreement there is likely to be about the statement. If there is absolute widespread agreement, it is a fact. “Booth killed Lincoln in the theater” is a fact. You could also say that Booth’s gun caused Lincoln’s death and this would be an empirical statement despite the word cause in the sentence.

14. Causal… …statements would require going out into the world and testing whether something causes something else through observation. “Public opinion causes changes in the ideological nature of Supreme court opinions” is a causal statement because we could go out and get measures of public opinion and Supreme Court outcomes and see whether there is a correspondence. This would be testing a causal claim. Causal claims must be falsifiable – like interpretive statements – but causal claims are also testable by collecting data. You should not be distracted by notions that the phenomena that are causally connected would be difficult to measure. If you can imagine a way to measure the two phenomena that may be causally related, then they are probably measurable.