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P202 stats > Second exam based on 7-10 > Flashcards

Second exam based on 7-10 Flashcards

1
Q

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

A

t-test

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2
Q

When can we conduct a z-test?

A

we know the population mean and standard deviation

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3
Q

Step 2 NHST t-test

A

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

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4
Q

kurtosis

A

thickening of the tail in the t-distribution

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5
Q

How many distributions are apart of the t-distributions?

A

There are more than ine

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6
Q

What happens when our sample is smaller?

A

The more kurtotic the t-distribution is

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7
Q

What happens when the sample is bigger?

A

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

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8
Q

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

A

We are estimating the variance of population 2

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9
Q

What happens when an estimate is wrong in science?

A

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

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10
Q

How do we build room for error?

A

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

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11
Q

What do thicker tails do?

A

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

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12
Q

Rule 1 for t-distribution

A

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

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13
Q

Step 2 of t-distribution

A

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

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14
Q

S^2

A

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

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15
Q

What does a larger variance do?

A

it inflated the width of the distribution

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16
Q

In science we use what to estimate?

A

our sample

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17
Q

3rd rule of t-distribution

A

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

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18
Q

what does a bigger sample size do?

A

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

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19
Q

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

A

We do not when our sample is bigger

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20
Q

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

A

True

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21
Q

what is df?

A

df = N-1

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22
Q

3 rules to determine t-distribution

A

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

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23
Q

Step 3 of NHST

A

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

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24
Q

Step 4 of NHST

A

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

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25
Q

Step 5 of NHST

A

decide whether to reject the null hypothesis, present inferential stats

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26
Q

t-test for dependent means

A

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

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27
Q

dependent means

A

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

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28
Q

mean of comparison distribution

A

standardized score of zero

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29
Q

What would the null represent in dependent t-tests?

A

represents no change between times

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30
Q

t test independent means

A

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

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31
Q

naturally occurring groups

A

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

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32
Q

experimentally manipulated groups

A

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

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33
Q

null hypothesis for independent means

A

shows no difference between groups

34
Q

degrees of freedom for independent means

A

df = N-2

35
Q

one way anova

A

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

36
Q

anova

A

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

37
Q

In one way anova what variables do we have?

A

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

38
Q

categorical variable

A

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

39
Q

outcome variable

A

the outcome of the study, a continuium

40
Q

What does anova weigh?

A

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

41
Q

between groups

A

captures the differences between group averages

42
Q

within group variance

A

captures variance within the same groups

43
Q

what anova weighs is what?

A

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

44
Q

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

A

called sample means

45
Q

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

A

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

46
Q

what happens when there are wider distributions?

A

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

47
Q

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

A

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

48
Q

F-ratio

A

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

49
Q

What happens if the F-ratio is 1?

A

likely no real differences

50
Q

F-ratio > 1 are there differences?

A

yes real differences

51
Q

Anova Step 1 NHST

A

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

52
Q

Anova step 2 of NHST

A

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)….

53
Q

Anova NHST step 3

A

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

54
Q

NHST anova step 4

A

convert our data into f-ratio

55
Q

Anova NHST step 5

A

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

56
Q

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

A

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

57
Q

Significant Omnibus

A

gives us permission to probe for differences in follow up tests

58
Q

follow up tests are both

A

comparisons & contrasts, they mean the same thing

59
Q

follow up tests are either

A

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

60
Q

pairwise

A

comparing two specific means from two specific groups

61
Q

complex

A

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

62
Q

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

A

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

63
Q

MCP

A

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

64
Q

MCP (dunn-bonferroni)

A

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

65
Q

MCP (tukey)

A

planned or post hoc comparisons, pairwise comparisons

66
Q

MCP (Scheffé)

A

planned or post hoc comparisons
pairwise or complex comparisons

67
Q

How do you chose an MCP?

A

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

68
Q

two-way anova

A

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

69
Q

What are the main effects in two way anova

A

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

70
Q

How do we compare main effects?

A

using marginal means, they are margins of the table

71
Q

A x B interaction effect

A

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

72
Q

When is there an interaction effect in cell means?

A

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

73
Q

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

A

tells us there is no interaction

74
Q

How are main effects tested?

A

through marginal means, the averages of the cell means

75
Q

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

A

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

76
Q

two-way anova step 1 NHST

A

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

77
Q

AxB (2x2) interaction

A

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

78
Q

two way anova step 2 NHST

A

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)….

79
Q

two way anova NHST step 3

A

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

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

Interaction effect -> F(1,df)

80
Q

two way anova step 4 NHST

A

calculate 3 f ratios

81
Q

two way anova step 5 NHST

A

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

82
Q

what reveals complexity and conceals it?

A

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

P202 stats (3 decks)

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