Psyc_315_-_Winter_2010_-_Class_13

Psyc_315_-_Winter_2010_-_Class_13 - Sampling distributions...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Sampling distributions and Standard error Chapters 9 & 11 2 Sample vs. Population • Focus will now shift to sample means , rather than individual scores. • Population : • Complete set of individuals, objects, or scores that the investigator is interested in studying. • In an actual experiment, the population is the larger group of individuals from which the experimental subjects have been taken. • The group that you want to draw inferences about. • Sample : • A subset of the population from which the experimental data is collected. 3 Examples of Samples and Population 4 Why Study Samples? • Often not practical to study an entire population. • Instead, researchers attempt to make samples representative of the populations. – Random Selection: • Each member of the population has an equal chance of being sampled. • Good but difficult. – Haphazard Selection: • Take steps to ensure samples do not differ from the population in systematic ways. • Not as good but much more practical. 5 Population and Sample • General strategy in Psychology: – Study a group of individuals who are believed to be representative of the general population (or some particular population of interest) • A “parameter” is a characteristic of a population. – Example : the average heart rate of all Canadians. • A “statistic” is a characteristic of a sample. – Example : the average heart rate of a sample of Canadians • We use statistics of samples to estimate parameters of populations. 6 Statistics and Parameters • Statistic b estimates b parameter Population M (or μ) Population SD Population SD 2 ρ “rho” M SD SD 2 r 7 Random Sampling Model • Takes into account chance factors (sampling variation) when sample results are being interpreted. • A common problem in statistical inference involves making inferences about population mean from M. • A random sample is a sample drawn from a population such that each possible sample of the specified size has an equal probability of being selected. – Telephone or house-to-house interviews conducted only in the evening, which automatically eliminates people who hold night jobs....
View Full Document

This note was uploaded on 04/29/2010 for the course PSYCH 315 taught by Professor Afroditipanagopoulos during the Winter '10 term at Concordia Canada.

Page1 / 8

Psyc_315_-_Winter_2010_-_Class_13 - Sampling distributions...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online