Second exam based on 7-10

Question 1

What test does not require us to know the standard deviation population?

Answer 1

t-test

Question 2

When can we conduct a z-test?

Answer 2

we know the population mean and standard deviation

Question 3

Step 2 NHST t-test

Answer 3

comparison of distribution is still distribution if means, now it’s a t-distribution (thicker tails)

Question 4

kurtosis

Answer 4

thickening of the tail in the t-distribution

Question 5

How many distributions are apart of the t-distributions?

Answer 5

There are more than ine

Question 6

What happens when our sample is smaller?

Answer 6

The more kurtotic the t-distribution is

Question 7

What happens when the sample is bigger?

Answer 7

the more the t-distribution looks like a normal z-distribution

Question 8

Why does the shape of our comparison distribution change from a z to a t distribution

Answer 8

We are estimating the variance of population 2

Question 9

What happens when an estimate is wrong in science?

Answer 9

we have to build room for error, in case it is wrong

Question 10

How do we build room for error?

Answer 10

we change the shape of our distribution, the room for error is done so by thickening the tails

Question 11

What do thicker tails do?

Answer 11

Thicker tails make it harder to reject the null hypothesis, they push farther out from the mean, they help with the possibility if it is wrong

Question 12

Rule 1 for t-distribution

Answer 12

u(m)=u(2)
the mean of the t-distribution is the same for the population 2

Question 13

Step 2 of t-distribution

Answer 13

We use variance for the sample population 1 to pop 2, we assume they both have the same variance
S^2 = E (x-m) ^2 /N-1
=
SS/N-1 = variance

Question 14

S^2

Answer 14

estimate of pop. 2 variance using our sample to estimate, instead of diving by N, we divide by N-1 to give room for error

Question 15

What does a larger variance do?

Answer 15

it inflated the width of the distribution

Question 16

In science we use what to estimate?

Answer 16

our sample

Question 17

3rd rule of t-distribution

Answer 17

shape defined by degrees of freedom (df), our sample determines our distribution

Question 18

what does a bigger sample size do?

Answer 18

it helps us to not have to estimate, instead it will give us a more accurate distribution

Question 19

When do we not need more room for error or a kurtosis tail?

Answer 19

We do not when our sample is bigger

Question 20

The bigger the sample the better estimate and the thinner tails can be?

Answer 20

True

Question 21

what is df?

Answer 21

df = N-1

Question 22

3 rules to determine t-distribution

Answer 22

1) Um = (variance)
2) S^2m= (SD)
3) Shape =t( ) (Degrees of freedom)

Question 23

Step 3 of NHST

Answer 23

critical cutoff score .05, look at the table with df and match it with it, look for one or two tail

Question 24

Step 4 of NHST

Answer 24

determine sample score on comparison distribution
t= M-ú/Sm

Question 25

Step 5 of NHST

Answer 25

decide whether to reject the null hypothesis, present inferential stats

Question 26

t-test for dependent means

Answer 26

comparing two means at two times, always dependent on each other

Question 27

dependent means

Answer 27

measuring a sample of participants at time one and we measure the same sample for time two

Question 28

mean of comparison distribution

Answer 28

standardized score of zero

Question 29

What would the null represent in dependent t-tests?

Answer 29

represents no change between times

Question 30

t test independent means

Answer 30

looking at two means from two separate groups, either naturally occurring or experimentally manipulated groups

Question 31

naturally occurring groups

Answer 31

ex: people living in the u.s and people living in canada

Question 32

experimentally manipulated groups

Answer 32

by researchers, one randomly group is assigned to receive some sort of treatment, the other is a control group with no treatment

Question 33

null hypothesis for independent means

Answer 33

shows no difference between groups

Question 34

degrees of freedom for independent means

Answer 34

df = N-2

Question 35

one way anova

Answer 35

compares three groups ex: clean record, dirty record & no info

Question 36

anova

Answer 36

when we want to compare three or more groups we conduct an analysis of variance

Question 37

In one way anova what variables do we have?

Answer 37

one categorical explanatory variable (A) one continuous outcome variable (Y)

Question 38

categorical variable

Answer 38

also called group membership, it explains variation in an outcome variable

Question 39

outcome variable

Answer 39

the outcome of the study, a continuium

Question 40

What does anova weigh?

Answer 40

b/w group variance vs. within group variance to see if the former outweighs the latter

Question 41

between groups

Answer 41

captures the differences between group averages

Question 42

within group variance

Answer 42

captures variance within the same groups

Question 43

what anova weighs is what?

Answer 43

at the heart of understand variation in the social and behavioral sciences

Question 44

what are the little x’s at the bottom of the anova graphs?

Answer 44

called sample means

Question 45

What happens when the sample means are different from each other?

Answer 45

due to sampling error, because the sample was too big it will not match with population mean

Question 46

what happens when there are wider distributions?

Answer 46

more variation within groups, also will create differences among sample averages

Question 47

When the little x’s are spread out on a graph?

Answer 47

both within and between variances are influencing them with real differences within population means and reflected in sample means

Question 48

F-ratio

Answer 48

between (estimate) / within (estimate) filters out the fake differences seen in sample averages

Question 49

What happens if the F-ratio is 1?

Answer 49

likely no real differences

Question 50

F-ratio > 1 are there differences?

Answer 50

yes real differences

Question 51

Anova Step 1 NHST

Answer 51

restate research question, H(0): u1 = u2 = u3 H(1): ~ (u1 = u2 = u3) (not the case that the three population means are the same)

Question 52

Anova step 2 of NHST

Answer 52

comparison distribution is a f-distribution defined by two degrees of freedom F(df b, df w) df b =a-1 df w= df(1)+df(2)+df(3)….

Question 53

Anova NHST step 3

Answer 53

only one tail in a f-distribution, because it includes f-ratios with a lower limit of 0, critical cutoff is found on an f-table df b/ df w

Question 54

NHST anova step 4

Answer 54

convert our data into f-ratio

Question 55

Anova NHST step 5

Answer 55

decide whether to reject the null hypothesis F(df b, df w) =X.XX, p < .05

Question 56

What happens when rejecting a null hypothesis in a one-way anova?

Answer 56

it tells us the population means are not the same, but we want to know which subgroups are different from others

Question 57

Significant Omnibus

Answer 57

gives us permission to probe for differences in follow up tests

Question 58

follow up tests are both

Answer 58

comparisons & contrasts, they mean the same thing

Question 59

follow up tests are either

Answer 59

pairwise or complex and a priori (planned) or a posteriori (post hoc)

Question 60

pairwise

Answer 60

comparing two specific means from two specific groups

Question 61

complex

Answer 61

tells us it’s not pairwise but we are always comparing two averages

Question 62

What happens when one conducts more than one follow up test?

Answer 62

requires research’s to adjust for inflation of their alpha level using a multiple comparison procedure (MCP)

Question 63

MCP

Answer 63

it is a procedure to show we are conducting multiple comparisons, to adjust our alpha level back to 0.05

Question 64

MCP (dunn-bonferroni)

Answer 64

planned comparisons, pairwise or complex comparisons. divide overall target level / by comparisons

Question 65

MCP (tukey)

Answer 65

planned or post hoc comparisons, pairwise comparisons

Question 66

MCP (Scheffé)

Answer 66

planned or post hoc comparisons pairwise or complex comparisons

Question 67

How do you chose an MCP?

Answer 67

if more than one MCP is appropriate, it is acceptable to choose the MCP with best results

Question 68

two-way anova

Answer 68

we break groups up in two different times, two categorical explanatory variables (A/B), one continuous outcome variable (Y)

Question 69

What are the main effects in two way anova

Answer 69

main effect of factor A, B interaction effects of A x B

Question 70

How do we compare main effects?

Answer 70

using marginal means, they are margins of the table

Question 71

A x B interaction effect

Answer 71

when comparing factor A & B it is called moderation, it shows whether factor B changes factor A

Question 72

When is there an interaction effect in cell means?

Answer 72

when they go in different directions such as top row goes up and bottom row goes down

Question 73

When graphing for a two way anova what does parallel lines tell us?

Answer 73

tells us there is no interaction

Question 74

How are main effects tested?

Answer 74

through marginal means, the averages of the cell means

Question 75

in two way anovas how many f-ratio or f-tests

Answer 75

3 for each because there are 3 effects (2 main & 1 interaction)

Question 76

two-way anova step 1 NHST

Answer 76

main effect of factor A pop 1: jurors told they have record pop:2 jurors told they have no record H(0): u(1) = u(2) H(1): ~ (u 1 = u 2) main effect of factor B pop 1: no legal experience pop 2: legal experience

Question 77

AxB (2x2) interaction

Answer 77

the effect of factor A on Y varies as a function of factor B

Question 78

two way anova step 2 NHST

Answer 78

df A =a-1 (a is the number of groups (2) df B= b-1 df AxB = Ncells -df (a)-df (b)- 1 df w =df(1) +df(2) + df(3)….

Question 79

two way anova NHST step 3

Answer 79

main effect of A -> F(1,df) main effect of B -> F(1,df) Interaction effect -> F(1,df)

Question 80

two way anova step 4 NHST

Answer 80

calculate 3 f ratios

Question 81

two way anova step 5 NHST

Answer 81

1) present inferential stats 2) present a statistical interpretation 3) present a substantial interpretation

Question 82

what reveals complexity and conceals it?

Answer 82

interaction effects (reveal it) main effects (conceal it)