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can show how variables relate to one another, help us to draw conclusions about a population based on a sample.
The extent to which the sample estimates will be distributed around the population parameter
how close our findings match what we should find in the population
*error decreases as population increases
Assumptions of standard error
- 1. Samples must be drawn from a population
- 2. Assumes simple random sampling
- 3. Assumes 100% completion rate
Hypothesis, Statement that tells us how we expect variables to be related.
Null hypothesis, statement that says there is no relationship.
Type I Error
When Null is true, but is rejected
Type II Error
When null hypothesis is false, but we mistakenly fail to reject.
How do you prevent a type I error?
set a standard for .05
To prevent a type II error
- have a larger sample
- use interval/ratio data
- use a directional hypothesis--say it goes one way instead of saying its different
1 categorical variable
eg. do political ads attack policy or character more often
- IV: 1 categorical variable with TWO groups
- DV: 1 continuous variable (amount of disclosure)
**Analysis of variance
- IV: 1 categorical variable with 3+ groups
- DV: 1 continuous variable (number of hours on homework)
**Test of association
- IV: A continuous variable (exam grade)
- DV: A continuous variable