In american society, romantic love is considered the main reason for people to marry. on the other hand, in some societies, romantic love is considered a form of obsession or madness. in fact, even in many societies where "true love" is not considered a form of insanity, arranged marriages are still the norm. for example, in many cultures it's believed that marriage is basically an economic arrangement. thus, a farmer needs a wife both to bear children and to share the farm work while raising new "hands" as additional farm labor. in other societies, marriage is mainly about cementing connections between families. indeed, most marriages between members of european royal or aristocratic lineages were primarily aimed at creating trust, loyalty, and mutual obligations between privileged and powerful families. which phrase signals a contrast between ideals about marriage? a. indeed b. in other societies c. on the other hand d. for example

early greek culture has had an important impact on government and education. first of all, they established the democratic political systems and procedures we have today. consequently, we inherited trial by jury for example. considering education, greek philosphers, mathematicians set he intellectual basis of different areas of studies. in addition, the alphabet originated from the phoenician alphabet into the one we have today and the first library was built in egypt. moreover, greek culture has an impact in current art, architecture and sports disciplines.

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).

“chain of settlements” belong to your dad