Chapter 23

Question 1

What is probability?

Answer 1

The likelihood that any one event will occur, given all the possible outcomes
Essential to understand inferential statistics
Represented by a lowercase p
Relationship to normal distribution
Implies uncertainty – what is likely to happen

Question 2

What is sampling error?

Answer 2

The tendency for sample values to differ from population values
The variance properties of a sampling distribution of means

Question 3

What are confidence intervals?

Answer 3

Estimating population parameters
- point estimate (mean)
Range of scores that is likely to contain the population parameter, with a certain level of confidence

Question 4

How do you interpret confidence intervals?

Answer 4

The correct interpretation of a 95% confidence interval is that if we were to repeat sampling many times, 95% of the time our confidence interval would contain the true population mean.
It is NOT correct to say that there is a 95% probability that the population mean falls within an obtained confidence interval. Increase confidence by decreasing precision (e.g., 99% confidence interval)

Question 5

What is a type I error?

Answer 5

Mistakenly finding a difference
Level of significance
Alpha (α)- probability of making a Type I error
Interpreting probability values
- The p value is the probability of finding an effect
as big as the one observed when the null
hypothesis is true.

Question 6

What is a type II error?

Answer 6

Mistakenly finding no difference
Beta (β)
Statistical power
* 1 – β
* Power is the probability that a test will lead to rejection of the null hypothesis, or the probability of attaining statistical significance.

Question 7

What are the determinants of statistical power?

Answer 7

P = power (1 – β)
A = alpha level of significance
N = sample size
E = effect size

Knowing three of these four will allow for determination of the fourth

Question 8

What is a priori analysis?

Answer 8

estimates sample size

Question 9

What is a post hoc analysis?

Answer 9

determines power

Question 10

What is a two-tailed test?

Answer 10

Test for nondirectional hypothesis
Allows for possibility that difference may be positive or negative

Question 11

What is a one-tailed test?

Answer 11

Tests for a directional hypothesis
More powerful
Should only be used when the relevant difference is only in one direction

Question 12

What are assumptions for parametric statistics?

Answer 12

Samples are randomly drawn from a parent
population with a normal distribution
Variances in the samples being compared are
roughly equal
Data should be measured on the interval or ratio
scales

Question 13

When can nonparametric statistics be used?

Answer 13

when parametric assumptions are not met

Question 14

What is a null hypothesis?

Answer 14

The first explanation is that the observed difference between the groups occurred by chance. This is the null hypothesis (H0), which states that the group means are not different. No matter how the research hypothesis is stated, the researcher’s goal will always be to statistically test the null hypothesis.

Question 15

What is a research hypothesis?

Answer 15

The second explanation for the observed findings is that the treatment is effective and that the effect is too large to be considered a result of chance alone. This is the alternative hypothesis (H1) These statements predict that the observed difference between the two populations means is not due to chance.
It is non-directional.

Question 16

Why do we analyze power?

Answer 16

To estimate sample size during the planning stages of a study and to determine the probability that a Type II error was committed when a study results in a nonsignificant finding.

Question 17

What is a z-ratio?

Answer 17

The z-ratio can be understood as a relationship between the difference between means (the numerator) and the variance within the sample (the denominator). The ratio will be larger as the difference between means increases and as variance decreases.