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. letâs 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% âtruthâ about the answers to these questions is to ask every single possible voter â something that is impossible, expensive, time consuming etc. instead, we take samples and use statistics to make âbest guessesâ 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 â we have to test what is known as the ânull hypothesisâ. as an example, the hypothesis might be âobama is more popular than romneyâ while the null hypothesis is âthere is no difference in popularity between obama and romneyâ. 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 âsignificanceâ). if it does, then we âaccept the null hypothesisâ 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).
