PSY 210 Final Study Guide

Value that describes a sample

Set of all inividuals of interest in a particular study

Set of individuals selected from a population, usually inteded to represent the population in a research study

A value that describes a population

• Independent variable: the variable being manipulated by the researcher
• Dependent variable: the one that is observed to assesss the effect of the treatment

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• Add all of the scores in the group.

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• Compute inside parentheses first, then square it.

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• Square each score, then add up squared scores.

Frequency distribution

histogram

bar graph

polygraph

Positively skewed

Negatively skewed

leptokurtic

platykurtic

The score or category has the greatest frequency

The score that divides the distibution in half

Arithmetic average

• Mean
• Incorporate every score

variance = mean squared deviation =

• sum of squared deviations
• number of scores

Base probability percentage with z-score of normal distribution

• Example:
• Given:
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• What percent scored higher than you on a test?
• P(z>+1.00)=.1587 (15.87%)

Example: For a normal distribution, what z-score separated the top 10% from the rest of the distribution?

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• Z=+1.28

• Standard distance between M and
• How well a sample mean estimates its population mean
• Smaller standard error, better estimation
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• A research deisng that uses a separate group of participants for each treatment condition (or for each population)
• AKA between-subjects deisgn

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• Estimated Error:
• Pooled Variance:
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A statistical technique that is used to measure and describe a relationship between two variables

NO manipulation of variables, just looking if 2 variable have a relationship to each other

2 variables that you are comparing move in the same direction

2 variables that you are comparing move in opposite directions

• Inferences about population from statistics that you calculated
• Test hypotheses about population parameters
• Require a numerical score for each individual
• Require data measured on an intercal or ratio scale

• Do not make statements about relationships to population parameters
• Data can be measured on nominal or ordinal scales
• Require fewer assumptions
• Are less sensitive than parametic tests