PSY295 Exam 2 Flashcards Preview

PSY > PSY295 Exam 2 > Flashcards

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

Counterbalancing

Solution to carry-over effects. Increases variation of scores, order effects into disturbance variables which affect DV scores but don't vary systematically w/ IV.