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Lecture 3

PSYC 202 Lecture 3: STATS 202 (week 3)
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7 Pages
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Department
Psychology
Course Code
PSYC 202
Professor
Ronald R Holden

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September 27 2017 WEEK 3 Sampling error is not making mistakes, but rather the variation that comes with taking a sample Key Ideas: Statistical inference among populations: o Defining a population o Selecting a sample from the population o Taking measurements on the individual units o Carry out statistical analysis, including descriptive statistics and inferential statistics that we use to make some statement about the population itself or to compare among different populations Sampling The aim of an idea sampling process is to select a group of units thats a good representation for the statistical population Ideal sampling process can be broken down into 4 components: o Units have known and nonzero probability of being included in your sample o Unbiased Bias is when your sample ha some systematic difference from the true statistical population that your trying to find out about Some of your units are less likely to be compared then some of your others o Independent Selecting one sampling unit to be in your sample should not influence if other sampling units should be included as well o Each possible sample has equal chance of being selected Making sure the individuals are mixed properly and representatively and that each possible sample has an equal chance of being selected DEFINIIONS: 1. Selection unit: every unit in the statistical population must have a chance of being in your sample 2. Bias: selection of units cannot inadvertently favour one outcome over another on average 3. Independence: selection of one unit cannot influence the probability that another unit is selected 4. Equal chance for all samples: every combination of units in must be possible in your sample A volunteer based sample is different form a simple random sample because: Some people may have no chance of being included
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