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**preview**shows page 1. to view the full**4 pages of the document.**Lecture 1

Some definitions

- Population

•The set of ALL the individuals of interest in a particular study

•Vary in size, but can be quite large

•Populations are described by parameters

- Sample

•A set of individuals SELECTED from a population

•Usually intended to represent the population in a research study

•Samples are described by statistics

- This distinction is very important in stats: the formulas change depending on whether you

are dealing with a population or a sample

Sampling error

- A sample is never identical to a population!

•People are all weird. Everyone is different

•This weirdness causes data to shift in random ways

- Sampling error

•The discrepancy, or amount of error, that exists between a sample statistic and the

corresponding population parameter

Sampling error (textbook example)

- Imagine a population of 1000 Carleton PSYC 2002 students

- Parameters:

•Average age= 21.3

•Average IQ= 112.5

•65% female, 35% male

Sampling error

- Sample #1: Jen, Emily, Sue, Paul, Melissa

- Sample statistics:

•Average age= 19.8

•Average IQ= 104.6

•80% female, 20% male

Sampling error

- Sample #1: Kermit, Fozzie, Gonzo, Piggy, Camilla

- Sample statistics:

•Average age= 20.4

•Average IQ= 114.2

•40% female, 60% male

This is NOT surprising

- Small samples tend to have a lot of variability

- We should not be surprised if a small sample does not reflect the population

Variables and data

- Variable

•Characteristic or condition that changes

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