Never mind I figured it out. Lol

the answer as multiple choice is c

africa, the second-largest continent, is bounded by the mediterranean sea, the red sea, the indian ocean, and the atlantic ocean. it is divided in half almost equally by the equator. the continent includes the islands of cape verde, madagascar, mauritius, seychelles, and comoros.

africa’s physical geography, environment and resources, and human geography can be considered separately.

the origin of the name “africa” is greatly disputed by scholars. most believe it stems from words used by the phoenicians, greeks, and romans. important words include the egyptian word afru-ika, meaning “motherland”; the greek word aphrike, meaning “without cold”; and the latin word aprica, meaning “sunny.”

today, africa is home to more countries than any other continent in the world. these countries are: morocco, western sahara (morocco), algeria, tunisia, libya, egypt, sudan, chad, niger, mali, mauritania, senegal, the gambia, guinea-bissau, guinea, sierra leone, liberia, côte d’ivoire, ghana, burkina faso, togo, benin, nigeria, cameroon, central africa republic, equatorial guinea, gabon, congo, democratic republic of the congo, angola, namibia, botswana, south africa, lesotho, swaziland, mozambique, zimbabwe, zambia, malawi, tanzania, rwanda, burundi, uganda, kenya, somalia, ethiopia, djibouti, eritrea and the island countries of cape verde, madagascar, mauritius, seychelles, and comoros.

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the answer is that the political parties encourage people to caste vote.

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).