ch 8 review

Question 1

differences between means of the groups that are not the result of treatment

Answer 1

Standard error of the Difference between the 2 means

Question 2

Pooled variance

Answer 2

combines the variability that occurs within each group and is the first indicator of the amount of error present in analysis

Question 3

First indicator of the amount of error present in analysis

Answer 3

Pooled variance

Question 4

IF scores within each group are widely different from one another, these large differences contribute to a larger _______________________________________________ and to the over all ______

Answer 4

Standard error of the Difference between the 2 means, error

Question 5

The larger the error, the _________ the calculated t-value and the less likely that a t-value will be _____________.

Answer 5

smaller, significant

Question 6

to calculate t we divide what by what

Answer 6

The difference between the mean scores divided by the error variance( a.k.a. standard error of the different between two means- why? because mathematicians are assholes)

Question 7

When calculating the degrees of freedom to use for comparing a t value to the t-table we need the df. If a control group and treatment group each has 10 participants what are the degrees of freedom used?
What else do we need to know?

Answer 7

(n1-1) + (n2-1)= df
(10-1)+(10-1)=18
df= 18

We need to know what alpha was set at. Most likely .05 but if history repeats itself you should beware of random bullshit like .025.

Question 8

IF the T-value obtained is larger than the t-table critical value what can we conclude?

Answer 8

If out value is larger than the tcv then it is unlikely our results occurred by chance alone and we say the results are significant. Yippee.

Question 9

If results are significant how should we refer to p?

Answer 9

p < .05.

Question 10

What are the assumptions that all parametric tests are based on:

Answer 10

• the data to measure the DV are interval or ratio
• the underlying populations for each group are normally distributed (30 participants usually solves this)
• The variances for the populations are roughly equivalent (homogeneity of variance)

Question 11

If the variance of one population is twice as large as the other groups can you still assume homogeneity of variance?

Answer 11

Yes. You do not have to be concerned until one variance is 4 times larger than the other.

Question 12

What is a large enough sample size to reasonably assume we have met the requirements for normal distribution?

Answer 12

30

Question 13

If data is NOT interval or ratio what type of test should we use? give an example

Answer 13

non parametric statistical tests- Chi-square test

Question 14

If our sample sizes are unequal can we be sure of the robustness of our statistical test?

Answer 14

Yes, as long as variances are relatively similar we can be assured.

Question 15

Why were inferential techniques developed?

Answer 15

so we could use smaller groups rather than an entire population to test the effects of an IV.

Question 16

When we generalize t-test results back to the population there will still be an amount of error present. To account for this we make our estimates using a _______ ________.

Answer 16

Confidence interval

Question 17

What are we measuring when determining the magnitude of a treatment? What is a common formula used?

Answer 17

Effect Size, Cohen’s d

Question 18

Repeated Measures t-test is an __________ parametric test for differences between two levels of an ______________ _______________________ using one group of participants.

Answer 18

inferential, independent variable

Question 19

When we use a repeated measures test do we reduce or inflate the error? Why or why not?

Answer 19

reduce, because our denominator or error becomes smaller.

Question 20

What does the null hypothesis for a repeated measure t-test look like?

Answer 20

Ho: uD=0 because it says the population mean difference is equivalent to zero. NO difference between pre and post test.

Question 21

in a repeated measures t-test what do we divide by what to get the t value?

Answer 21

Divide the Mean Difference Score (MD by/ Estimated standard error of the mean difference score (Smd)