Lecture 7 Sampling Distributions &amp; Confidence Intervals

# Lecture 7 Sampling Distributions &amp; Confidence...

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Sampling Distributions & Confidence Intervals Lecture 7

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Exam Info.
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
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
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.
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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## This note was uploaded on 05/25/2010 for the course PSYCH 11 taught by Professor Ryne during the Spring '10 term at UC Riverside.

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Lecture 7 Sampling Distributions &amp; Confidence...

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