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Lecture 6

Political Science 3N06 Semester II 2014 Lecture 6a confidence intervals .docx

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Political Science
Todd Alway

Political Science 3N06 Semester II 2014: Lecture 6a Confidence Intervals - Because of the central limit theorem, we know that there is a 95% probability that a single randomly selected sample (provided that the sample is large enough) will have a mean that is within +- 1.96 standard errors of the true population mean - In other words, we can use sample statistics to estimate population parameters by using the properties of the sampling distribution Estimation Procedures: - Point estimates require you to simply use your sample statistic as your estimate of the population parameter - However, you know that your sample mean or sample proportion is unlikely to be exactly the same as your population mean or population proportion - So how close is your point estimate to the true population value? - AConfidence Interval contains a point estimate - However, it bounds that point inside of a range (or interval) - The size of the interval is a product of the Confidence level that you are aiming at - The more confident you want to be o The larger the interval has to be - The less confident you want to be o The smaller the interval will be - Lowering your confidence level shrinks the confidence interval down to a reasonable size - But it is a bit of a trade off - I am less certain - But I can be more precise in my estimate Steps to Calculating a confidence interval - Step one – select an alpha level (α) - This is the risk that you are willing to take of being wrong o i.e. that your conf
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