25 - Confidence intervals and two-sided tests: A level...

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Confidence intervals and two-sided tests: A level α two-sided significance test rejects a hypothesis H0: μ = μ0 exactly when the value μ0 falls outside a level 1 − α confidence interval for μ. When you use statistical inference, you are acting as if your data are a random sample or come from a randomized comparative experiment. Practical problems such as nonresponse in samples or dropouts from an experiment can hinder inference even from a well-designed study. Different methods are needed for different designs. The z procedures aren’t correct for random sampling designs more complex than an SRS. (Such as multistage or stratified) There is no cure for fundamental flaws like voluntary response surveys or uncontrolled experiments. The problem about the shape of the population distribution is less essential than where the data come from. There is one important exception to the principle that the shape of the population is less critical than how the data were produced. Outliers can distort the results of inference. Any
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This note was uploaded on 02/27/2012 for the course STAT 121 taught by Professor Patticolling during the Winter '11 term at BYU.

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25 - Confidence intervals and two-sided tests: A level...

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