PSY295 Exam 2 Flashcards Preview

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Flashcards in PSY295 Exam 2 Deck (20)
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1
Q

Sampling Error

A

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

2
Q

Distribution of sample means

A

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

3
Q

Central Limit Theorem

A

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
Q

Law of Large Numbers

A

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

5
Q

Standard Error of the Mean

A

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

6
Q

Type I Error

A

When treatment has no effect but you say it does.
Reject Ho but it is actually true.
False positive.
change scientific status quo.

7
Q

Type II Error

A

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
Q

Alpha

A

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

9
Q

One-sample z-test vs t-test

A

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
Q

One-tailed Test vs. a Two-Tailed Test

A

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
Q

Power

A

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
Q

T-distribution vs. Normal Distribution

A

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
Q

Independence Assumption

A

Observations within each sample must be independent.

14
Q

Normality Assumption

A

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

15
Q

Within-Subjects Study

A

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
Q

Between-Subjects Study

A

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

17
Q

Independent Groups T-Test

A

Used for between-subjects study.

18
Q

Homogeneity of Variance Assumption

A

The two populations being compared must have the same variance.

19
Q

Carry-over Effects

A

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

20
Q

Counterbalancing

A

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.