Lecture 7 Sampling Distributions & Confidence Intervals

Lecture 7 Sampling Distributions & Confidence...

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Sampling Distributions & Confidence Intervals Lecture 7
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Exam Info.
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Recap from Last Week Empirical vs. theoretical distributions Probability The normal distribution Using Z scores to find proportions
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Today Sampling issues Sampling distributions t distributions Confidence intervals
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Sampling issues Whenever we use a sample, there is a certain amount of uncertainty We use sampling distributions to measure this uncertainty.
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Samples Random sample A subset of a population chosen so that all samples have an equal chance of being selected. Biased sample Not all from the population have an equal chance of being selected. Non-random research samples
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Generalizability Why does the sample matter? Determines who we can generalize findings to. Truman vs. Dewey (1948) Public opinion polls Not random samples
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Sampling Distributions So far we have been talking about 1 sample, and from this, we calculate statistics such as M, S, r, etc. How confident can we be that this represents the whole population? There could be a distribution of possible samples!
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Sampling Distributions If we do this over and over again, we get a sampling distribution . Definition: a theoretical distribution of a statistic (like the mean or standard deviation) based on all possible random samples drawn from the same population.
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1 Sample vs. 1 Sampling Distribution 1 Sample- one distribution 4 Samples- 4 distributions; 1 sampling distribution
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Expected value The mean of a sampling distribution (the mean of all the sample means) Standard error The standard deviation of a sampling distribution
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Lecture 7 Sampling Distributions & Confidence...

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