What is the most likely reason bismarck was named chancellor when king wilhelm i became kaiser of germany? wilhelm wanted to reward bismarck, who was largely responsible for german unification. wilhelm did not want bismarck to have as much power as he had when he was prime minister. wilhelm feared bismarck would turn against germany if he did not receive a position of power in the new government. wilhelm knew bismarck was popular with both liberals and conservatives, which would unite the empire

really and truely i dont know sorry

the space race was a battle to make advanced technology to get to the moon and to get a better understanding of space.

the governor can not veto a referendum that the voters approve

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).