Week Five - Statistical Hypothesis Testing

Question 1

What do standardised scores help do?

Answer 1

Determine how extreme/unusual a score is and to compare data from different scales.

Question 2

Z-scores =

Answer 2

M = 0, SD = 1

Question 3

Method for converting raw scores to Z-scores

Answer 3

Subtract mean from individual score
Divide by standard deviation
𝑧 = 𝑥 − 𝑥 /SD

Question 4

Z scores allow what?

Answer 4

Comparison on different scales

Question 5

What is SD?

Answer 5

A quantity expressing by how much the members of a group differ from the mean value for the group.

Question 6

M ± SD describes

Answer 6

the distribution of a sample.

Question 7

For a normally distributed sample, M ± SD contains what percent of observed scores?

Answer 7

~68%

Question 8

Does sample size affect SD?

Answer 8

No. Size of the sample does not systematically affect SD.

Question 9

What is a SE?

Answer 9

The standard error is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation.

In statistics, a sample mean deviates from the actual mean of a population—this deviation is the standard error of the mean.

Measures how far the sample mean of the data is likely to be from the true population mean.

Question 10

What does a SE describe?

Answer 10

The variability of statistics and the expected distribution of statistics if sampling was repeated many times.

Question 11

Does sample size affect SE? What is the relationship?

Answer 11

SE is systematically affected by sample size
Inverse relationship
Bigger samples have smaller SE.

Question 12

What is a confidence interval?

Answer 12

A confidence interval is a range statistic. It provides a range within which we have a specified level of confidence that the true population value lies

95% CIs are the likely range within which the true value of the population parameter sits.

Question 13

Narrow 95% CIs indicate … while Wide 95% CIs indicate

Answer 13

high precision.

low precision.

Question 14

Can CI’s be used to describe sample distribution?

Answer 14

CIs cannot and must not be used to describe sample distribution.

Question 15

What should you use to describe the distribution of your sample?

Answer 15

Use SD if you want to describe the distribution of your sample.

Question 16

What is a p-value?

Answer 16

A p-value is the probability that a sample statistic as extreme or more extreme than the observed sample would occur if random variation is the only cause of variability.

Question 17

What p-value means statistical significance

Question 18

What is a Null Hypothesis?

Answer 18

When we assume the size of the observed effect is purely a result of random sampling (no, or nil effect).

Question 19

What is the alternative hypothesis?

Answer 19

The hypothesis you hope to support.
Can be directional (one-tailed).
Can be non-directional (two-tailed).

Question 20

When P =

Answer 20

Reject the null hypothesis and accept the alternative hypothesis.

Question 21

A p-value = >.05 means what?

Answer 21

A statistic as large or larger would occur more than 5% of the time IF only random sampling is responsible for the variation.

Question 22

when a p-value = >.05 we do what?

Answer 22

Fail to reject the null hypothesis.

Question 23

What is a Type I error?

Answer 23

False positive

A type 1 error occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.

Question 24

What is a Type II error?

Answer 24

False negative

A type II error occurs when a researcher fails to reject a null hypothesis which is really false. Here a researcher concludes there is not a significant effect, when actually there really is.