1. Lesson 10 - 1 The Language of Hypothesis Testing

2. Objectives Determine the null and alternative hypothesis from a claim Understand Type I and Type II errors State conclusions to hypothesis tests

3. Vocabulary Hypothesis – a statement or claim regarding a characteristic of one or more populations Hypothesis Testing – procedure, base on sample evidence and probability, used to test hypotheses Null Hypothesis – H 0, is a statement to be tested; assumed to be true until evidence indicates otherwise Alternative Hypothesis – H 1, is a claim to be tested.(what we will test to see if evidence supports the possibility) Level of Significance – probability of making a Type I error, α

4. Steps in Hypothesis Testing A claim is made Evidence (sample data) is collected to test the claim The data are analyzed to assess the plausibility (not proof!!) of the claim

5. Determining H o and H a H o – is always the status quo; what the situation is currently the claim made by the manufacturer H a – is always the alternative that you are testing; the new idea the thing that proves the claim false

6. Reality H 0 is TrueH 1 is True Conclusion Do Not Reject H 0 Correct Conclusion Type II Error Reject H 0 Type I Error Correct Conclusion H 0 : the defendant is innocent H 1 : the defendant is guilty Type I Error (α): convict an innocent person Type II Error (β): let a guilty person go free Note: a defendant is never declared innocent; just not guilty decrease α  increase β increase α  decrease β Four Outcomes from Hypothesis Testing

7. Hypothesis Testing: Four Outcomes We reject the null hypothesis when the alternative hypothesis is true (Correct Decision) We do not reject the null hypothesis when the null hypothesis is true (Correct Decision) We reject the null hypothesis when the null hypothesis is true (Incorrect Decision – Type I error) We do not reject the null hypothesis when the alternative hypothesis is true (Incorrect Decision – Type II error)

8. English Phrases (from Ch 6) Math SymbolEnglish Phrases ≥At leastNo less than Greater than or equal to >More thanGreater than <Fewer thanLess than ≤No more thanAt most Less than or equal to =ExactlyEqualsIs ≠Different from

9. Three Ways – H o versus H a 1.Equal versus less than (left-tailed test) H 0 : the parameter = some value (or more) H 1 : the parameter < some value 2. Equal hypothesis versus not equal hypothesis (two-tailed test) H 0 : the parameter = some value H 1 : the parameter ≠ some value 3. Equal versus greater than (right-tailed test) H 0 : the parameter = some value (or less) H 1 : the parameter > some value b a b a Critical Regions

10. Example 1 A manufacturer claims that there are at least two scoops of cranberries in each box of cereal Parameter to be tested: Test Type: H 0 : H a : left-tailed test  The “bad case” is when there are too few Scoops = 2 (or more) (s ≥ 2) Less than two scoops (s < 2) number of scoops of cranberries in each box of cereal  If the sample mean is too low, that is a problem  If the sample mean is too high, that is not a problem

11. Example 2 A manufacturer claims that there are exactly 500 mg of a medication in each tablet Parameter to be tested: Test Type: H 0 : H a : Two-tailed test  A “bad case” is when there are too few  A “bad case” is also where there are too many amount of a medication in each tablet  If the sample mean is too low, that is a problem  If the sample mean is too high, that is a problem too Amount = 500 mg Amount ≠ 500 mg

12. Example 3 A pollster claims that there are at most 56% of all Americans are in favor of an issue Parameter to be tested: Test Type: H 0 : H a : right-tailed test  The “bad case” is when sample proportion is too high population proportion in favor of the issue  If p-hat is too low, that is not a problem  If p-hat is too high, that is a problem P-hat = 56% (or less) P-hat > 56%

13. Example 4 You have created a new manufacturing method for producing widgets, which you claim will reduce the time necessary for assembling the parts. Currently it takes 75 seconds to produce a widget. The retooling of the plant for this change is very expensive and will involve a lot of downtime. H o : H a : TYPE I: TYPE II:

14. Example 4 H o : µ = 75 (no difference with the new method) H a : µ < 75 (time will be reduced) TYPE I: Determine that the new process reduces time when it actually does not. You end up spending lots of money retooling when there will be no savings. The plant is shut unnecessarily and production is lost. TYPE II: Determine that the new process does not reduce when it actually does lead to a reduction. You end up not improving the situation, you don't save money, and you don't reduce manufacturing time.

15. Summary and Homework Summary –A hypothesis test tests whether a claim is believable or not, compared to the alternative –We test the null hypothesis H 0 versus the alternative hypothesis H 1 –If there is sufficient evidence to conclude that H 0 is false, we reject the null hypothesis –If there is insufficient evidence to conclude that H 0 is false, we do not reject the null hypothesis Homework –pg 511-513; 1, 2, 3, 7, 8, 12, 13, 14, 15, 17, 20, 37