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Unformatted text preview: 2 of 13 1.2 Testing a claims when σ is known REVIEW What is a hypothesis test? A hypothesis test calculates the probability of observing our sample data as suming the null hypotheses H is true . If the probability is low enough ( pvalue ≤ α ), we reject H and have evidence to support our alternative hy pothesis H a . Eight simple steps 0. Write down what is known . 1. Determine which type of hypothesis test to use. 2. Check the test’s requirements . 3. Formulate the hypothesis : H , H a 4. Determine the significance level α . 5. Find the pvalue . 6. Make the decision . 7. State the final conclusion . KTRHSPDC: “Know The Right Hypothesis So People Don’t Com plain” What is the pvalue? The pvalue represents the probability of observing our sample data assuming H is true. We use the sampling distribution to determine if sampling error could explain our observed sample statistic’s deviation from H ’s claim about the parameter. If we decide to reject H , the pvalue is the actual Type I error for the study data. 1 The pvalue is calculated using the test statistic and it’s corresponding distribution. Common form of a test statistic test statistic = (sample statistic) (null hypothesis of parameter) (standard deviation of sample statistic) (1) What are the types of errors? Type I error α / pvalue occurs when we reject H but H is actually true . The pvalue is the Type I error. (ex. Convicting a innocent person — when H is innocence.) Type II error β occurs when we fail to reject H but H is actually false . (ex. Letting a criminal go free — when H is innocence.) 1.2 Testing a claims when σ is known USE Often used to help answer: 1 The α we use to make our decision is just the maximum Type I error we will accept. It is the significance level, not the actual Type I error. Anthony Tanbakuchi MAT167 Testing a claim about a population mean 3 of 13 1. Is the mean of a population equal to μ ? 2. Is the mean of a population different than μ ? COMPUTATION One sample hypothesis test, σ known. Definition 1.1 requirements (1) simple random sample, (2) σ known, (3) C.L.T. applies. null hypothesis H : μ = μ alternative hypothesis (1) H a : μ 6 = μ , (2) H a : μ < μ , (3) H a : μ > μ test statistic : described by the z distribution z = ¯ x μ σ/ √ n (2) Question 3 . What does the test statistic z represent?...
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This note was uploaded on 02/10/2012 for the course MAT 1670 taught by Professor Tanbakuchi during the Spring '11 term at University of Florida.
 Spring '11
 Tanbakuchi
 Statistics, Probability

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