Define the following terms

1. Define the following terms Null Hypothesis: P-value: Significance Level: Statistically Significant: Pg 489

2. More about Tests! Remember, you are not proving or accepting the null hypothesis. Most of the time, the null means no difference or no change from the previous parameter. If we reject the null, we are concluding that there has been a change or something affected the outcome. As a Statistician, you usually want to prove the alternative hypothesis. i.e. Think of a pill to help lose weight. The null hypothesis might be  = 0 lbs lost and the alternative hypothesis would be  > 0. The company that produces the pill would want to prove the alternative hypothesis to justify their claim that their pill helps people lose weight. Pg 473-4

3. Confidence Intervals and Hypothesis Tests A confidence interval can be used instead of a test. If the parameter (null hypothesis) is in the interval, then we would fail to reject the null hypothesis. -scores to These values are called critical values. That means if your Z score is more extreme (farther right or left) of these values, you will reject Ho. Pg 479-81

4. Making Errors on Hypothesis Tests There are two types of errors a statistician can make when doing tests: I. The null hypothesis is true, but we mistakenly reject it. II. The null hypothesis is false, but we fail to reject it. These two types of errors are called Type I and Type II errors. Pg 482-84

5. Positive in Statistics is rejecting Ho. Type I error = false positive Type II error = false negative Type II error are more critical in statistics as we can to correctly reject a false Ho

6. Possible outcomes Reality Ho is True Ho is false Reject Ho Fail to Reject Ho Outcome of test

7. The probability of making a Type I error = . • That is the probability of rejecting a true null hypothesis is . Why is that? • The probability of making a Type II error = β. • We will not have to calculate β, but we need to know a few facts about what will effect β. The probability of a Type II error can be lowered by: • Having a larger  • Having a larger sample size • the true parameter being significantly different than the null hypothesis. (The distance between the true parameter and the null hypothesis is called the effect size.) Pg 482-84

8. Power of a test: The power of a test is the probability that a test correctly rejects a false null hypothesis. Power = 1 – β • The Power of a test is increase by lowering β. • Therefore, the following will increase the power: • Having a larger  • Having a larger sample size • If the true parameter is significantly different than the null hypothesis (Effect Size is large) Pg 487-88

9. Example: A bank wants to encourage more customers to make payments on delinquent balances by sending them a video tape urging them to set up a payment plan. Based on their results, the statistician for the bank found a 90% confidence interval for the success rate to be (0.29, 0.45). Their old send-a-letter method had worked 30% of the time. • Is this evidence that the video worked better than the old method? Why or why not? • What is a Type I error in the context of this situation? • What is a Type II error in the context of this situation? • If the video tape strategy really works well, actually getting 60% of the customers to set up a payment plan, would the power of the test be higher or lower compared to a 32% pay off rate? Explain.

10. Example: A game of chance at a carnival is supposed to be fair. A random sample of carnival goers played the game and 110 out of 250 won. • Estimate the true proportion of winning using a 99% confidence interval. • Does your confidence interval provide evidence that the game is unfair? Explain. • What is the significance level of this test? • What would a type I error be in context? • What would a type II error be in context? • How could you as the statistician increase the power of this?