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Biostatistics > Exam II Material > Flashcards

Exam II Material Flashcards

1
Q

distributions can be

A

skewed

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2
Q

skewed data can be

A

negative or positive

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3
Q

distributions that are peaky or taily are called

A

kurtosis

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4
Q

distributions that are very flat with long tails are called

A

platykurtic

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5
Q

distributions that are very pointy/peaky are called

A

leptokurtic

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6
Q

distributions that are just right are called

A

mesokurtic

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7
Q

3 ways to test to determine is data is normal

A
  1. whether data is normal enough depends on what you will do with the data
  2. there arent as many hard rules
  3. key is to justify what you are doing
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8
Q

what are tests of normality

A

kolmogorov- smirnov and shapiro-wilk

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9
Q

what are kolmogorov- smirnov and shapiro-wilk very sensitive to

A

n

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10
Q

kolmogorov- smirnov and shapiro-wilk, between these two what is considered better

A

S-W

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11
Q

what are Q-Q plots

A

a good visual method for double-checking data especially for large n

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12
Q

when do we not consider our data normal for skewness and kurtosis

A

if skewness and kurtosis are more than 2x their standard error

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13
Q

when would we consider alternate tests for skewness and kurtosis

A

3x standard error

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14
Q

what is correlation

A

describes a relationship between two variables

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15
Q

how should we do correlation by hand

A

arrange data in order of one of the quantitative variables

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16
Q

correlations are a descriptor….

A

of how reliably a change in one variable predicts change in another variable

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17
Q

what are positive relationships

A

ones where an increase in one variable predicts an increase on the other

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18
Q

what are negative relationships

A

ones where the an increase in on variable predicts a decrease in the other

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19
Q

is there always a relationship in correlation

A

no

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20
Q

correlation alone cannot be used to make a

A

definitive statement about causation

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21
Q

correlation can be found in almost

A

everything

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22
Q

what is the most effective way of presenting relationship data

A

scatterplots

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23
Q

what are relationships best described by lines

A

linear relationships

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24
Q

what relationships are best described with curves

A

curvilinear relationships

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25
Q

how can we quantify a correlation

A

by the pearson product moment correlation

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26
Q

the pearson correlation varies from

A

-1 to 1

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27
Q

what is the number in pearson with the weakest/ no correlation

A

0

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28
Q

whats the number for the strongest correlation

A

1

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29
Q

what indicates the direction of the correlation in pearson

A

the sign

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30
Q

positive sign means

A

positive correlation

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31
Q

negative sign means

A

negative correlation

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32
Q

assumptions of the pearson correlation (5)

A
  1. uses two variables
  2. variables are both quantitative (ratio/ interval)
  3. variable relationships are linear
  4. minimal skew/ no large outliers
  5. must observe the whole range for each variable
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33
Q

what should you not do when working with correlation

A

do not bin data, use the raw scores/ values

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34
Q

in correlation set up with will be comparing two different variables for the..

A

same set of cases

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35
Q

in pearson correlation output p< = 0.05 means

A

significant

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36
Q

parametric analysis includes

A

pearson

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37
Q

both variables are ratio/ interval and normal

A

pearson

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38
Q

nonparametric analysis includes (3)

A
  1. spearman’s rank
  2. kendall’s tau-b
  3. ETA
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39
Q

: appropriate for ordinal and skewed data

A

spearman’s rank

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40
Q

appropriate for ordinal and skewed data, generally
considered superior to Spearman (especially for small groups) and is less
affected by error

A

kendall’s tau-b

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41
Q

a special coefficient used for curvilinear relationships, particularly good for nominal by interval analyses

A

ETA

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42
Q

an entire, comprehensive group

A

population

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43
Q

a subset of the population, used to infer things about the population

A

sample

44
Q

random samples are not casual or haphazard, getting truly random samples requires care

A

sampling

45
Q

for characteristics of populations in regression, we might the true

A

n

46
Q

do you know the mean in population in regression

A

you might be able to estimate

47
Q

do you know the standard deviation in regression for population

A

we probably dont

48
Q

for samples in regression we always know

A

n, mean, and st

49
Q

what do regression and correlation have in common

A

both are about relationships between variables and work best with quantitative variables

50
Q

regression differs from correlation in that we have explicit “………” variables used to estimate the value of some target variable

A

predictor

51
Q

do you need strong evidence of causality that with correlation

A

yes

52
Q

regression is primarily calculated by analyzing

A

error

53
Q

a key to calculating regression is to look at the

A

predictive error for the y-axis variable

54
Q

the what of what is the key to calculating the regression line

A

sum of squares of the error

55
Q

the goal of regression is to find a best fit line that minimizes the

A

squares of the error

56
Q

assumptions of linear regression

A
  1. requires 2 or m ore scalar variables
  2. there is one dependent variable and one or more independent variables
  3. the relationships between the independent variables and dependent must be linear
  4. the data must be homescedastic
57
Q

property of a dataset having variability that is similar across it’s whole range

A

homoskedasticity

58
Q

opposite of homoscedastic is

A

heteroskedastic

59
Q

symbol for number of observations for a sample and a population

A

sample- n
population- n/N

60
Q

symbol for a datum for a sample and a population

A

sample- x
population- X

61
Q

symbol for mean of a sample and a population

A

sample- x bar
population- lu (mew)

62
Q

symbol for variance for a sample and a population

A

sample- s2/SD2
population- sigma squared

63
Q

symbol for standard deviation for a sample and a population

A

sample- s/SD
population- sigma

64
Q

what does R mean in a linear regression?

A

correlation between the observed values, and the ones the model predicts

65
Q

what does R2 mean in a linear regression ?

A

the amount of variability in the dependent variable that is accounted for by changes in ALL the independent variables

66
Q

what does unstandard B represent in a linear regression?

A

tells you the unit change in the dependent per unit change in the independent

67
Q

what does std err represent in a linear regression?

A

used in calculating the t

68
Q

what does beta tell you in a linear regression?

A

how strongly this variable predicts the dependent

69
Q

t and sig in a linear regression

A

tells you if the variable was a significant predictor of the dependent

70
Q

adjusted R^2 in a linear regression

A

If you have a lot of independent variables, you’ll get some relationships due to chance. This tries to correct for that

71
Q

std error of the regression in a linear regression

A

A measure of how accurately the model predicts the dependent variable

72
Q

population -

A

an entire comprehensive group

73
Q

sample-

A

a subset of the population
used to infer things about the population

74
Q

sampling-

A

random samples are not casual or haphazard, getting truly random samples requires care

75
Q

random sampling-

A

used when surveying
obtains a “snapshot” of the population
just because sampling is random doesn’t mean that your sample is perfectly represenattive

76
Q

random assignment-

A

a process used in an experiment to minimize bias in your experiment groups

77
Q

in both random sampling and random assignment, what does increasing n do?

A

it will decrease the likelihood of seeing a non-representative or biased sample

78
Q

what does probability tell us?

A

when events are common, vs when events are rare

79
Q

are common outcomes statistically significant ?

A

no

80
Q

what outcomes are considered “statistically significant”?

A

rare outcomes

81
Q

is probability arbitrary ?

A

yes - 100%

82
Q

the central limit theorem

A

“Regardless of the shape of the population, the shape of the sampling distribution of the mean approximates a normal curve if the sample size is large enough”

83
Q

does the sample tell us everything about the population?

A

no

84
Q

what is the criteria for the probability of obtaining any specific sample from a population to fit a normal curve?

A

if the sample is sufficiently large

85
Q

is it likely to get a very extreme sample?

A

no but it is possible
you will most likely get a mean somewhere near the actual population mean.

86
Q

what does the sampling distribution of the mean refer to?

A

the probability distribution of means for all possible random samples of size n for a population

87
Q

standard error of the mean (SEM)

A

describes the average amount of variability sample means have around the true population mean

88
Q

what does a z-test do?

A

converts a mean to a z-score (typically sufficiently rare)

89
Q

what magnitude is considered sufficiently rare?

A

greater than +_ 1.96

90
Q

alternative hypothesis / research hypothesis (H1)

A

states there is something special about the population being observed

91
Q

null hypothesis (H0)

A

states there is nothing special about the population being observed

92
Q

do we ever accept H1?

A

NO
we can only reject H0

93
Q

what do decision rules define

A

precisely when you reject H0 or not

94
Q

what do the design rules depend on?

A

types of study
the variables
tests performed
your field

95
Q

what is the significance level (alpha)?

A

the proportion of area under the curve considered “rare” for the purposes of your decision rule
originally set as a= 0.05

96
Q

what do we say when we do not have a significant result?

A

“we fail to reject” H0
this is a weak result

97
Q

what do we say when we of have a significant result ?

A

we definitely “reject H0”
this is a strong result

98
Q

what do we say when we keep or reject the null

A

keep: H0 could be true
reject: H0 is most likely false

99
Q

one tailed vs two tailed tests-

A

one tail- used not very often, retain H0 for all except for one side of the curve
two-tail- used more frequently, retain H0 for only middle of the curve, reject H0 for both ends

100
Q

when should you choose a one tail test?

A

-if you are positive that your hypothesis could only possibly result in a change in one direction
- if you are only interested in a change in one direction
- must be established as an experimental and analytical protocol before any analysis occurs
-if the consequences of being different in one

101
Q

why shouldn’t you choose a one-tailed test?

A

-if you do not have very strong justification, reviewers will be critical of your choice
- sometimes seen as a sketchy way of making something look significant

102
Q

in general, what is alpha ?

A

a trade off between two types of mistake
the choice of what alpha is is mostly arbitrary

103
Q

what is a type 1 error

A

false positive
equal to alpha, decreases as alpha decreases

104
Q

what is a type 2 error

A

miss/ mistake

105
Q

what does it mean for the null if p is greater than or equal to alpha?

A

we retain the null
it is not significant

106
Q

what does it mean when the p is less than or equal to alpha?

A

we reject the null
the data is significant

107
Q
A

Biostatistics (2 decks)

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