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Significance Testing

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Submitted By dh00535782
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Chapter 11: Testing a Claim

Objectives: Students will: Explain the logic of significance testing. List and explain the differences between a null hypothesis and an alternative hypothesis. Discuss the meaning of statistical significance. Use the Inference Toolbox to conduct a large sample test for a population mean. Compare two-sided significance tests and confidence intervals when doing inference. Differentiate between statistical and practical “significance.” Explain, and distinguish between, two types of errors in hypothesis testing. Define and discuss the power of a test.

AP Outline Fit:
IV. Statistical Inference: Estimating population parameters and testing hypotheses (30%–40%) B. Tests of significance 1. Logic of significance testing, null and alternative hypotheses; P-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power 4. Test for a mean (large sample -- ( known)

What you will learn:

A. Significance Tests for µ (( known) 1. State the null and alternative hypotheses in a testing situation when the parameter in question is a population mean µ. 2. Explain in nontechnical language the meaning of the P-value when you are given the numerical value of P for a test. 3. Calculate the one-sample z-statistic and the P-value for both one-sided and two-sided tests about the mean µ of a Normal population. 4. Assess statistical significance at standard levels α by comparing P to α. 5. Recognize that significance testing does not measure the size or importance of an effect. 6. Recognize when you can use the z test and when the data collection design or a small sample from a skewed population makes it inappropriate. 7. Explain Type I error, Type II error, and power in a significance-testing problem.

Section 11.1: Significance Tests: The Basics

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