“A society that puts equality—in the sense of equality of outcome—ahead of freedom will end up with neither equality nor freedom. The use of force to achieve equality will destroy freedom, and the force, introduced for good purposes, will end up in the hands of people who use it to promote their own interests. On the other hand, a society that puts freedom first will, as a happy by-product, end up with both greater freedom and greater equality. . . . A free society releases the energies and abilities of people to pursue their own objectives. It prevents some people from arbitrarily suppressing others. It does not prevent people from achieving positions of privilege, but so long as freedom is maintained, it prevents those positions of privilege from becoming institutionalized.” —Milton Friedman, Free to Choose (1980)

Which of the following statements is most consistent with the author’s argument in this passage?

a. Undermining freedom will extinguish both freedom and equality

b. There should be more emphasis on governmental action to achieve equality.

c. Individual freedom and equality are not compatible values.

d. Equality is not an important outcome in society.

a hypothesis is an expected answer to a question, usually based on some theory or prior research. hypothesis testing is a method of determining the viability of a hypothesis. let’s use the us presidential election as an example. there are many hypotheses (questions) about how people feel or whom they will vote for. the only way to know the 100% “truth” about the answers to these questions is to ask every single possible voter – something that is impossible, expensive, time consuming etc. instead, we take samples and use statistics to make “best guesses” about the answer to a particular hypothesis. we never know for sure if our conclusion is right at the time we accept/reject it, but these techniques, when used appropriately can get us close to the truth most of the time (which is a lot better than guessing, flipping coins or throwing darts to get answers).

the tricky part about hypothesis testing is that we have to think somewhat backwards about it – we have to test what is known as the “null hypothesis”. as an example, the hypothesis might be “obama is more popular than romney” while the null hypothesis is “there is no difference in popularity between obama and romney”. we focus on the null hypothesis because it tells us exactly what the expected value of our tests should be (obama popularity = romney popularity) and we can collect data and see if it comes out that way (see next section on “significance”). if it does, then we “accept the null hypothesis” and reject our original hypothesis. if the null hypothesis is disproven, we look at the data to see if our original hypothesis holds (obama> romney).

i think the answer is d hope this