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HYPOTHESIS TESTING. In statistics, a hypothesis is a claim or statement about a property of a population.A hypothesis test (or test of significance) is a standard procedure for testing a claim about a property of a population.. RARE EVENT RULE. This chapter, as the last chapter, relies on the Rare
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1. Sections 7-1 and 7-2 Overview
Basics of Hypothesis Testing
2. HYPOTHESIS TESTING
3. RARE EVENT RULE
4. OBJECTIVES FOR SECTION 7-2 Given a claim, identify the null hypothesis and the alternative hypothesis, and express both in symbolic form.
Given a claim and sample data, calculate the value of the test statistic.
Given a significance level, identify the critical value(s).
Given the value of the test statistic, identify the P-value.
State the conclusion of a hypothesis test in simple, nontechnical terms.
Identify type I and type II errors that could be made when testing a given claim.
5. NULL HYPOTHESIS
6. ALTERNATIVE HYPOTHESIS
7. NOTE ABOUT IDENTIFYINGH0 AND H1
8. NOTE ABOUT FORMING YOUR OWN CLAIMS
9. TEST STATISTIC
10. CRITICAL REGION
11. SIGNIFICANCE LEVEL
13. TWO-TAILED TEST
14. LEFT-TAILED TEST
15. RIGHT-TAILED TEST
16. P-VALUE
18. CONCLUSIONS
19. DECISION CRITERION
20. WORDING THE FINAL CONCLUSION
21. TYPE I ERROR
22. TYPE II ERROR
24. CONTROLLING TYPE I AND TYPE II ERRORS For any fixed a, an increase in the sample size n will cause a decrease in ß.
For any fixed sample size n, a decrease in a will cause an increase in ß. Conversely, an increase in a will cause a decrease in ß.
To decrease both a and ß, increase the sample size.