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Unformatted text preview: 11/3/09 Lecture 17 1 STOR 155 Introductory Statistics Lecture 17: Hypothesis Testing Section 6.2 The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 11/3/09 Lecture 17 2 • Point estimation – to estimate a parameter (quick and easy) • Confidence interval – to estimate a parameter with measures for reliability and accuracy attached • Hypothesis testing – hypothesis: a statement about the parameters – to assess whether the data provide enough evidence for some claim about the population Procedures for statistical inference 11/3/09 Lecture 17 3 Confidence Interval • Point estimate with margin of error • Confidence interval for a population mean – Assumption: the population variance is known – Confidence level C determines z * n z x n z x * * , 11/3/09 Lecture 17 4 Hypothesis Testing • Sometimes you may not be interested in estimating the parameter value … • Rather, you have some claim ( belief ) about the parameter and you want to see whether the data support the claim or not, i.e. to choose between two decisions: `` support ’’ versus `` contradict ’’ 11/3/09 Lecture 17 5 “Biased” oneEuro Coin? • A group of statistics students spun the Belgian oneEuro coin 250 times, and heads came up 140 times. • p = P (H) in each spin • Claim : the coin is biased (more specifically, p is greater than 0.5) 11/3/09 Lecture 17 6 • Two hypotheses – H : the null hypothesis • the statement of “no effect” or “no difference” • the statement we try to find evidence against – H a : the alternative hypothesis • the statement we hope or suspect is true • Usually H a is based on a ``hint’’ from samples Concepts of Hypothesis Testing 11/3/09 Lecture 17 7 “Biased” oneEuro Coin? (continued) • Sample: 140 heads among 250 spins of a Belgian oneEuro coin (a hint ) • p = P (H) in each spin • H : p = 0.5 vs H a : p > 0.5....
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This note was uploaded on 11/17/2011 for the course STOR 155 taught by Professor Andrewb.nobel during the Fall '08 term at UNC.
 Fall '08
 AndrewB.Nobel

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