PSY1022 WEEK 12 DISC 6

Question 1

PROBABILITY VS INFERENTIAL STATISTICS

Answer 1

Probability is used to predict what kind of samples are likely to be obtained from a population.
Inferential statistics used to make generalizations about a population from a sample.

Question 2

SAMPLING DISTRIBUTION OF MEANS

Answer 2

A frequency distribution showing all possible sample means that occur when samples of a particular size are drawn from a population.
A sampling distribution is approximately a normal distribution.

Question 3

CENTRAL LIMIT THEOREM

Answer 3

A statistical principle that defines the mean, standard deviation, and shape of a theoretical sampling distribution.

Question 4

STANDARD ERROR OF THE MEAN

Answer 4

The standard deviation of the sampling of the means.

Question 5

SAMPLING ERROR

Answer 5

The difference, due to chance, between a sample statistic and the population parameter it represents.

Question 6

MEAN OF A SAMPLING DISTRIBUTION

Answer 6

The mean of the sampling distribution equals the mean of the underlying raw score population from which we create the sampling distribution.
Population mean so use µ.

Question 7

STANDARD DEVIATION OF THE SAMPLING DISTRIBUTION

Answer 7

Is mathematically related to the standard deviation of the raw score population.

Question 8

REGION OF REJECTION

Answer 8

That portion of a sampling distribution containing values considered too unlikely to occur by chance, found in the tail or tails of the distribution.

Question 9

CRITERION

Answer 9

The probability that defines whether a sample is unlikely to have occurred by chance and thus is unrepresentative of a particular population.
Determines the size of the region of rejection.
Usually 0.5 (5%)
But if doing +ve and -ve then 2.5 each end.
Represented by alpha α.

Question 10

CRITICAL VALUE

Answer 10

The score that marks the inner edge of the region of rejection in a sampling distribution; values that fall beyond it lie in the region of rejection.

Question 11

INFERENTIAL STATISTICS

Answer 11

Procedures for determining whether sample data represent a particular relationship in the population.

Question 12

PARAMETRIC STATISTICS

Answer 12

Inferential procedures that require certain assumptions about the raw score population represented by the sample; used to compute the mean of the scores.

Question 13

NON PARAMETRIC STATISTICS

Answer 13

Inferential procedures that do not require stringent assumptions about raw score population represented by the sample data.

Question 14

EXPERIMENTAL HYPOTHESES

Answer 14

Two statements made before a study is begun, describing the predicted relationship that may or may not be demonstrated by the study.

Question 15

TWO-TAILED TEST

Answer 15

The test used to evaluate a statistical hypothesis that predicts a relationship but not whether the scores will increase or decrease.

Question 16

ONE-TAILED TEST

Answer 16

The test used to evaluate a statistical hypothesis that predicts that scores will only increase or only decrease.

Question 17

STATISTICAL HYPOTHESES

Answer 17

Two statements that describe the population parameters the sample statistics will represent if the predicted relationship exists or does not exist.

• alternative hypothesis
• null hypothesis

Question 18

ALTERNATIVE HYPOTHESIS

Answer 18

The hypothesis describing the population parameters that the sample data represent if the predicted relationship does exist.
- It says the changing the independent variable produces the predicted difference in populations.

Question 19

NULL HYPOTHESIS

Answer 19

The hypothesis describing the population parameters that the sample data represent if the predicted relationship does not exist.

Question 20

Z-TEST

Answer 20

The parametric procedure used to test the null hypothesis for a single-sample experiment when the true standard deviation of the raw score population is know.

Question 21

SIGNIFICANT

Answer 21

Describes results that are too unlikely to accept as resulting from sampling error when the predicted relationship does not exist; it indicates rejection of the null hypothesis.
- results ARE in the region of rejection.

Question 22

NONSIGNIFICANT

Answer 22

Describes results that are considered likely to result from chance sampling error when the predicted relationship does not exist; it indicates failure to reject the null hypothesis.
- results are NOT in the region of rejection.

Question 23

TYPE I ERRORS

Answer 23

Deciding to reject the null hypothesis when the null hypothesis is true (that is, when the predicted relationship does not exist)
- conclude that a treatment has an effect, when it doesn’t.

Question 24

TYPE II ERRORS

Answer 24

Deciding to retain the null hypothesis when the null hypothesis is false (that is, when the predicted relationship does exist)

• test fails to detect a treatment
• can’t determine probability for it.
• represented by β (beta)

Question 25

POWER

Answer 25

The probability that we will detect a true relationship and correctly reject a false null hypothesis; the probability of avoiding a Type II error. Closely related to Type II error. Power = 1 - β Power of 0.8 = good.

Question 26

EFFECT SIZE ESTIMATE

Answer 26

Hypothesis testing doesn't tell us how big the effect size is, so use effect size estimate. Measured using Cohen's d.

Question 27

COHEN'S D

Answer 27

Tells us the degree of separation between two distributions. - how far apart the means are (in standardised units). d = mean difference / standard deviation

Question 28

MAGNITUDE OF D

Answer 28

0. 2 = small effect 0. 5 = medium effect 0. 8 = large effect

Question 29

STEPS IN HYPOTHESIS TESTING

Answer 29

- State the hypothesis (eg. µH1: blah blah ≠ 100) - Set a critical region (At α = 0.5, critical region z=±1.96) - Collect data and compute sample statistics (sample score z-score = 2.33) - Make a decision (reject the null hypothesis?)

Question 30

ALPHA

Answer 30

Is the probability of committing a Type I error. | - concluding a treatment has an effect when it doesn't.

Question 31

ONE-TAIL TEST

Answer 31

Null hypothesis: µBlah ≤ 100 Alternative hypothesis: µBlah > 100 Alpha is 0.5, but only at one end.

Question 32

ONE-TAIL TEST vs TWO-TAIL TEST

Answer 32

One: - use when theory or previous studies predict direction. Or logic says. - allows you to reject null with a smaller difference in the specified direction Two: - competing theories predict opposing outcomes - you want a conservative assessment - no strong expectation either way Two tail is more common.

Question 33

INCREASING POWER

Answer 33

- Use a higher alpha level. 1. 0 is greater than 0.5 = bigger critical region - Use a one-tail test - Increase your sample size - A larger treatment effect will also result in greater power

Question 34

ASSUMPTIONS FOR HYPOTHESIS TESTING USING THE Z-TEST

Answer 34

1. Random sampling 2. Independent observations (no event has any influence on another) 3. The SD is not changed by the treatment 4. Normal sample distribution.