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Step 1: The null hypothesis is that the mean number of days spent in Atlantic City by visitors i

A srate the mall and aliernatine hypothens you fivand in the...

State the null and alternative hypothesis you found in the lab. H_0: μ = 2.59 H_1: μ < 2.59 Will this be a left, right, or two tailed test? z-test for one mean w/ known population standard deviation will be used State the value of the test statistic score z = (238 - 259) / (1.21 / sqrt(176)) = -0.21 / 10.96404589 = -230245026 z = -2.303 Calculate the p-value P(-2.303) = 0.0107 a = 0.05 P < 0.05 P < 0.0107 < 0.05 P = 0.0107 Test our claim for the 0.01 level of significance. Interpret the decision made in the conclusion, in the context of our claim (make sure you use the phrase "reject" or "fail to reject") Reject the null hypothesis Test our claim for the 0.05 level of significance. Interpret the decision made in the conclusion, in the context of our claim (make sure you use the phrase "reject" or "fail to reject") P < 0.05 P < 0.0107 < 0.05 Reject the null hypothesis Suppose our claim is instead that the mean number of days spent in Atlantic City by visitors is not equal to 2.59 Less than 259 days a = 0.005 z = -1.645

Transcript

00:01 All right, so you have some questions.

00:03 We're asked about p values and hypothesis tests.

00:07 So the first one, how do we interpret the p value of 0 .022? well, this, it provides strong evidence that professor rose's class outperformed the other exam fires across the nation.

00:19 Because she was testing, or she's making the claim, she leaves her students outperform the students over the nation.

00:32 So this p value is saying, hey, this is the probability of seeing the results she did if her students perform the same.

00:42 So it's a low probability.

00:43 That's right.

00:43 It's a strong evidence that they've outperformed the other exam.

00:59 Right? so now moving on.

01:02 And by right, i mean they take the test.

01:05 This is after they write the exam.

01:06 For students, it's going out of this.

01:07 So after they take the test.

01:10 So here we have the null and alternative hypothesis.

01:13 This hypothesis is given for the beef is safe to the null, the beef is not safe for the alternative.

01:20 The inspector concludes that the beef is safe when it is actually not safe.

01:25 This is a type 2 error.

01:28 Type 1 is where we is our alpha is what your p values.

01:33 That's your type 1 error.

01:35 Your probability of seeing your result if the null is true.

01:41 So but the type 2 area, that's failing to reject when you actually should.

01:49 So here, the inspector said it's safe.

01:52 That's failing to reject.

01:54 This is the failing to reject part.

01:59 This is this part.

02:00 Concluding the beef is safe.

02:02 When it's actually not safe, this is the h not is false.

02:10 Failing to reject when you should.

02:14 In this case, failing to reject means you're saying the people is safe.

02:16 But it's not safe.

02:18 All right, moving on.

02:20 The answer is that it's the probability of observing a mean of 510 or more under the assumption that the null is true.

02:27 That's exactly what the p value is.

02:33 The p value is the probability of seeing your result or more extreme under the null hypothesis condition.

02:42 If that null hypothesis is true, that's this one.

02:46 The fourth one, she has a p value of 0 .24 on her study, and that means she should fail to reject.

03:03 Granted, i mean, we're not given the level of significance, but it's reasonable to not reject.

03:10 Because they're going back to forward...

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