Flashcards in PSY295 Exam 2 Deck (20):

1

## Sampling Error

###
The discrepancy, amount of error, between a sample statistic and its corresponding population parameter.

--S

2

## Distribution of sample means

### The collection of sample means for tall the possible random samples of a particular size (n) that can be obtained from a population.

3

## Central Limit Theorem

### Distribution of sample means for sample size n will have mean of mew and sd of sigma/sqrt of n, and will approach a normal distribution as n approaches infinity.

4

## Law of Large Numbers

### The larger the sample size, the closer the sample mean will be closer to the population mean.

5

## Standard Error of the Mean

### Measures the standard amount of difference between M and mew that is reasonable to expect simply by chance.

6

## Type I Error

###
When treatment has no effect but you say it does.

Reject Ho but it is actually true.

False positive.

change scientific status quo.

7

## Type II Error

###
Treatment has effect but you say it doesn't.

Fail to reject Ho but it is really false.

False negative.

Less problematic bc affects are still out there to be found.

8

## Alpha

### Level of significance: probability value that is used to define the very unlikely sample outcomes if the null hypothesis is true.

9

## One-sample z-test vs t-test

###
Z-test: when both mew and sigma of comparison population are known.

T-test: when sigma is not known but can be found using sample data as estimate.

10

## One-tailed Test vs. a Two-Tailed Test

###
One-tailed: directional: specify either an increase/decrease in population mean score. They make a statement about the direction of the effect.

Two-tailed: does not say anything about direction of the effect, simply that it is not within the parameters of Ho.

11

## Power

###
Probability that the test will reject the null hypothesis if the treatment really has an effect.

--High N = more power

--Stronger treatments = more power

--One-tailed = more power

--Bigger alpha = more power.

12

## T-distribution vs. Normal Distribution

### T-distribution: changes with degrees of freedom. As df gets very large, t-diet gets closer in hale to a nomad diet. T are more variable, tends to be flatter and more spread out.

13

## Independence Assumption

### Observations within each sample must be independent.

14

## Normality Assumption

### The two populations from which the samples are selected must be normally distributed.

15

## Within-Subjects Study

### Repeated-measures study: a single sample of individuals is measured more than once on the same dependent variable. Same subjects are used in all of the treatment conditions.

16

## Between-Subjects Study

### Variation from subject to subject. Each person has only one level of that variable.

17

## Independent Groups T-Test

### Used for between-subjects study.

18

## Homogeneity of Variance Assumption

### The two populations being compared must have the same variance.

19

## Carry-over Effects

### Occurs when a subject's response in the second treatment is altered by lingering aftereffects from the first treatment.

20