Study Guides (380,000)
CA (150,000)
Carleton (5,000)
PSYC (800)

PSYC 2002 Study Guide - Statistical Inference, Statistic, Sampling Error

Course Code
PSYC 2002
Steven Carroll

This 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
You're Reading a Preview

Unlock to view full version