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Inferential statistics
can show how variables relate to one another, help us to draw conclusions about a population based on a sample.

Standard error
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

H_{1}
Hypothesis, Statement that tells us how we expect variables to be related.

H_{0}
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 hypothesissay it goes one way instead of saying its different

ChiSquare
1 categorical variable
eg. do political ads attack policy or character more often

TTest
 IV: 1 categorical variable with TWO groups
 DV: 1 continuous variable (amount of disclosure)

ANOVA
**Analysis of variance
 IV: 1 categorical variable with 3+ groups
 DV: 1 continuous variable (number of hours on homework)

Correlation
**Test of association
 IV: A continuous variable (exam grade)
 DV: A continuous variable

