The russian leader who brought massive reform to the soviet union beginning in 1985 was

it was certainly a debatable decision, mainly because it was based primarily on political - and not on legal or social - views. since the congress at that time was led by members of the republican party, and the territory that the tribes wanted to turn into an exclusively indian state was predominantly democratic, the proposal (as expressed in the constitution) did not succeed, but the constitution itself proved to be of crucial importance in the creation of the state of oklahoma, and, most importantly, it guaranteed the involvement of representatives of the indian tribes in the government of the future joint state.  

as a member of congress, i would have voted to ratify the constitution, or at least to have it tabled. ignoring it was an interested and partial decision.  

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).