![]() ![]() How do we calculate a probability of getting a result thisĮxtreme or more extreme? How do we even measure that? And this is where we're going Of getting this result or something even moreĭifferent than what's expected is less than the significance level, then we'd reject the null hypothesis. And those thresholds you have seen before. Is below some threshold, then we tend to reject the null hypothesis and accept an alternative. Of getting a result at least this extreme? And if that probability These hypothesis tests, is say what's the probability Of getting this result, even assuming that the And just through random chance, it might have just So if you just look at this, just look, hey, maybe there'sĪ higher frequency of D, but maybe you'd say, well, But let's say our actual results, when we look at these 100 items, we get that A is theĬorrect choice 20 times, B is the correct choice 20 times, C is the correct choice 25 times, and D is the correct choice 35 times. The A to be the correct choice, 25 times B to be the correct choice, 25 times C to be the correct choice, and 25 times D to be the correct choice. Where A is a correct choice would be 25% of this 100. Remember, in any hypothesis test, we start assuming that the So there's four differentĬhoices, A, B, C, D and a sample of 100. And if this doesn't make sense yet, we'll see it in a second. And then this would be the expected number that you would expect. So this is the correct choice, correct choice. And let's write down the data that we get when we look at that sample. And so let's say we takeĪ sample of 100 items. Of your potential items here, and you could take a sample. To actually test this? Well, we've seen this show before, at least the beginnings of the show. Now, what would be ourĪlternative hypothesis? Well, alternative hypothesis would be not equal distribution, not equal distribution. Or another way of thinking about it is A would be correct 25% of the time, B would be correct 25% of the time, C would be correct 25% of the time, and D would be correct 25% of the time. Hypothesis is equal distribution, equal distribution of correct choices, correct choices. How could you test this? Well, you could start with a null and alternative hypothesis, and then we can actuallyĭo a hypothesis test. Now, let's say you have a hunch that, well, maybe it is skewed It essentially is a 25%Ĭhance of any of them. That the correct answer for any one of the items is A, B, C, or D. And the test makers assureįolks that, over many years, there's an equal probability Say there's some type of standardized exam where every question on the test has four choices, choice A, choice B, choice C, and choice D. ![]()
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