Intro to Biostats Flashcards

1
Q

a research perspective which states there will be no difference between the comparison groups

A

null hypothesis H0

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2
Q

statistical perspectives that can be taken by the researcher in their alternative hypothesis

A

superior
noninferior
equal

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3
Q

alpha error

A

type I error

rejecting the null when you should accept it

false positive

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4
Q

beta error

A

type II error

accepting null when you should reject it

false negative

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5
Q

define power

A

statistical ability of a study to detect a true difference when it exists

“accuracy”

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6
Q

the ______ the sample size, the greater the ability of _________ .

A

greater

ability to detect a true statistical difference

increase in power

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7
Q

the smaller the difference between group, that is required to show a statistical difference, then the greater _________ is needed.

A

greater sample size is needed

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8
Q

when determining sample size you should anticipate …….?

A

drop outs and lost to follow up

so oversample in the beginning to compensate

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9
Q

bell curve percentages based on standard deviation

A

1 stdD = 68%
2 stdD = 95%
3 = 99.7%

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10
Q

probability value = ?

A

p value

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11
Q

the probability value is selected before or after the study starts?

A

before

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12
Q

if the p value is lower than the alpha value, then we say?

A

alpha value = 5%, 1%, etc.

we say it is statistically significant

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13
Q

relate p value to a statistically significant test

A

the p value is lower than alpha

so we reject the null (not accept)

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14
Q

relate a p value less than the alpha of 5%, and the risk of type I error

A

p value is lower, we reject the null

therefore, the risk of experiencing a type I error is acceptably low = less than 5%

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15
Q

at 95% confidence and p value of 0.005, what is the risk of error?

A

.5% risk of being wrong

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16
Q

relate a p value of 0.01% and 3 groups

A

there is at least one significant difference between the 3 groups

typically between control and the most extreme group

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17
Q

should baseline data be statistically significant or not different?

A

should show no statistical difference

to show that our experiment groups are not different so final results will show a difference only if my intervention caused it

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18
Q

a p value of 0.91, what is your chance of being wrong?

A

91% chance of being wrong when you say there is a statistical difference (type I error)

if you claim a difference you have a 91% chance of being wrong

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19
Q

when do we want p values to not be statistically different

A
  1. when comparing baseline characteristics at start

2. When using a levene’s test

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20
Q

3 primary level for variables - data types

A

nominal
ordinal
interval/ratio

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21
Q

3 key attributes of data measurement

A

order/magnitude
consistency of scale (equal distance)
rational absolute zero

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22
Q

nominal

A

no order
no consistency of scale

simply work w/ no quantitative characteristics

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23
Q

any question that only has 2 categories is always what type of data?

A

nominal

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24
Q

ordinal

A

has order
no consistency of scale

ex. pain scale, stress levels, happiness ratings

disagree, somewhat disagree, neutral, etc.

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25
Q

interval/ratio data

A

has order
has consistency of scale
ratio has absolute zero

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26
Q

interval data

A

arbitrary zero value
0 does not mean absence

temperature

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27
Q

ratio data

A

has an absolute zero
0 = absence

ex. 0 heartbeat = dead

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28
Q

after data is collected, we can appropriately go _____ in specificity/detail of data measurement levels, but never ____ .

A

go down

but never up

in terms of nominal, ordinal, interval, ratio

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29
Q

measures of dispersion/spread

A

mean, median, mode
outliers
min/max and range
IQR

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30
Q

difference between variance and standard deviation

A

variance is the distance from the mean of one particular value

standard deviation represents a % of data being this far from the mean

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31
Q

relate bell graph to percentiles

A

broken into 4 25% sections about the median (=50th percentile)

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32
Q

IQR

A

interquartile range
Q1 - Q3 = IQR
25th - 75th percentile = IQR

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33
Q

statistical tests used on normally distributed data is called ?

A

parametric tests

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34
Q

positively skewed

A

tail pointing to the right/positive direction

mean > median

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35
Q

negatively skewed

A

tail pointing to the left/negative direction

mean < median

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36
Q

if the data is not skewed then how are the mean and median related?

A

they should be the same/ almost the same

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37
Q

what are the 3 ways to tell if the data is skewed?

A
  1. are the mean and median the same?
  2. what does the graph look like?
  3. what is the skew value?
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38
Q

skewness value

A

if data is not skewed it will be as close to zero as possible

can have pos./neg. values

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39
Q

kurtosis

A

a measure of extent to which the data clusters about the mean

normal distribution, kurtosis = 0

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40
Q

positive kurtosis

A

= higher clustering about the mean

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41
Q

negative kurtosis

A

= less clustering about the mean

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42
Q

discrete vs. continuous data

A

discrete is solid numbers whilst continuous can have decimals

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43
Q

required assumptions for interval/ratio data

A
  1. normally distributed
  2. equal variances
  3. randomly derived and independent
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44
Q

levene’s test

A

tells us if interval/ratio data is normally distributed w/ equal variances or not

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45
Q

what if interval data is not normally distributed?

A

just use a non-parametric test

or transform the data using z-scores (log transformations)

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46
Q

variables required when interpreting a p value

A
  1. is it significant
  2. who was higher/lower
  3. by how much?

include all three, no specific order

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47
Q

______ must be equal in order to pick an interval test.

A

variances

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48
Q

levene’s test is used to assess whether ______ are equal between ?

A

variances between all groups

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49
Q

before running a levene’s test you need? and why

A

need the null hypothesis stating there is no difference

we want the p value to come back not significant to prove the variances are equal

if we prove they’re equal data can then be treated as interval data

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50
Q

number of siblings is an example of _____ data

A

interval data

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51
Q

define confidence interval

A

an interval around the p value that we are %% confident that the true difference is within this range

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52
Q

a CI that includes reducing and increased risk

A

means that it is not significant because a significant test cannot show both directions

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53
Q

when interpreting a CI for OR/RR, and the range contains 1.0

A

is not significant

if range crosses 1.0 then it is not significant

54
Q

when interpreting CI for actual data values, and crosses zero

A

it is not significant

55
Q

does statistical significance actually confer meaningful, _____ significance?

A

clinical significance

56
Q

4 key questions to selecting the correct statistical test

A
  1. what data level is being recorded?
  2. what type of comparison/assessment is desired?
  3. how many groups being compared?
  4. is the data independent or related/paired?
57
Q

correlation tests

A

provides quantitative measure of strength & direction of a relationship between variables

ranges from -1 to +1

refers to line graphs/ slope

58
Q

nominal correlation test

A

contingency coefficient

59
Q

ordinal correlation test

A

spearman correlation

60
Q

interval correlation test

A

pearson correlation

61
Q

running a partial correlation

A

to control for confounding

62
Q

contingency coefficient

A

for nominal correlation testing

63
Q

spearman correlation

A

for ordinal correlation testing

64
Q

pearson correlation

A

for interval correlation testing

65
Q

what do correlation tests tell you?

A

tell you the relationship between two variables

66
Q

gender is _____ data

A

nominal

67
Q

a test to determine event-occurrence or time to event

A

survival test

68
Q

what does survival describe

A

the lack of the “event” occurring

69
Q

survival test

A

compares the proportion of events over time, or time to events
between groups

70
Q

nominal survival test

A

log-rank test

71
Q

ordinal survival test

A

cox-proportional hazards test

72
Q

interval survival test

A

Kaplan-meier test

73
Q

log-rank test

A

nominal survival test

74
Q

cox-proportional hazards test

A

ordinal survival test

75
Q

Kaplan-meier test

A

interval survival test

76
Q

Kaplan-meier curve

A

a graphical representation of a survival test

all tests can provide this

(even tho interval survival test has this same name)

77
Q

see changes over time

A

survival test

78
Q

testing for outcome predictions or associations

A

regression testing

79
Q

regression

A

provides a measure of relationship between variables and allows the prediction about the dependent/outcome, when the independent is known

can use several variables to increase prediction

80
Q

regression tests also calculate ?

A

OR for a measure of association

81
Q

nominal regression test

A

logistic regression

82
Q

predict whether you do or do not get something

A

nominal regression

only two options

83
Q

if the outcome variable (dependent variable) is ordinal data type

A

then choose ordinal regression test

84
Q

ordinal regression test

A

multinominal logistic regression

85
Q

if the outcome variable is of the interval data type

A

then use interval regression test

ex. predicting actual gpa number

86
Q

interval regression test

A

linear regression

87
Q

logistic regression

A

nominal regression test

88
Q

multinominal logistic regression

A

ordinal regression test

89
Q

linear regression

A

interval regression test

90
Q

want to predict the likelihood of some outcome

A

regression testing

91
Q

what do you evaluate to determine what type of regression test to run?

A

only what data type the outcome variable is

92
Q

univariate

A

unadjusted OR

93
Q

ordinal data - 2 groups of independent data

A

mann-whitney test

94
Q

ordinal data - >3 groups of independent data

A

Kruskal-wallis test

95
Q

ordinal independent data

A
  • -both tests compare the median values between groups
  • -also used for non parametric interval data
  • -if 3+ groups are significant must do a post-hoc test to determine differences
96
Q

3+ group comparison that is significant - ordinal independent data

A

then you must do a post-hoc test to determine what the differences are

97
Q

ordinal data - 2 groups of paired/related data

A

Wilcoxon signed rank test

98
Q

ordinal data - 3+ groups of paired/related data

A

friedman test

99
Q

ordinal paired/related data

A
  • -both tests compare median values
  • -can be used for non parametric interval tests
  • -if significant in 3+ tests must do post-hoc test
100
Q

key words for paired/related data

A

pre vs post
before vs after
baseline vs end

101
Q

ordinal data - post-hoc tests for 3 or more group comparisons

A

student-newman-keul test

Dunnett test

dunn test

102
Q

student-newman-keul test

A

compares all pairwise comparisons possible

all groups must be equal in size

post-hoc test for ordinal data

103
Q

Dunnett test

A

compares all pairwise comparisons against a single control

all groups must be equal in size

post-hoc test for ordinal data

the other two tests find all comparisons possible - this is everything vs one specific thing

104
Q

dunn test

A

compares all pairwise comparisons possible

useful when all groups are not of equal size

post-hoc test for ordinal data

105
Q

ordinal post-hoc test: when groups are not equal in size

A

dunn test

106
Q

ordinal post-hoc test: to find all comparisons possible

A

student-newman-keul test

dunn test

107
Q

interval data: 2 groups of independent data

A

student t-test

108
Q

interval data: 3+ groups of independent data

A

analysis of variance - ANOVA

109
Q

interval data - independent data

A

tests compare means of all groups against dependent variable

–must do post-hoc test when 3+ group comparison is significant

110
Q

interval data: 3+ groups of independent data w/ confounders

A

analysis of Co-variance - ANCOVA

111
Q

ANCOVA

A

compares the means of all groups against a dependent variable while also controlling for the co-variance of confounders

for interval independent data w/ confounders

112
Q

how many groups can an ANOVA analyze?

A

any number of groups

113
Q

interval data: 2 groups of paired/related data

A

paired t-test

compares the mean values between groups that are related

114
Q

interval data: 3+ groups of paired/related data

A

repeated measures of ANOVA

compares the means of all groups of related data against a dependent variable

must do post-hoc if significance is found

115
Q

interval data: post-hoc tests for 3+ group comparisons

A

student-newman-keul test
Dunnett test
dunn test

same explanations as ordinal data
plus

tukey/scheffe tests
Bonferroni correction

116
Q

tukey/scheffe test

A

interval post-hoc test

compares all pairwise comparisons possible
groups must be equal in size

117
Q

tukey vs. scheffe tests

A

tukey – more conservative than the stu.n.k

scheffe – less affected by violations in normality and homogeneity of variance
*most conservative

118
Q

Bonferroni correction

A

adjusts the p value for # of comparisons being made
very conservative

interval data post-hoc test

119
Q

validation/assessment committee

A

kappa statistic

kappa interpretation

120
Q

kappa statistic

A

a correlation test

shows relationships between evaluators

121
Q

kappa interpretation

A
\+1 = the observers perfectly classify everyone the same way
0 = no relationship between observers classifications above what is expected by chance
-1 = observers classify everyone exactly the opposite of each other
122
Q

kappa K

A

value can be + or -

meaning good agreement or poor agreement

123
Q

kappa test significance

A

to determine if the decisions of the observers is consistent amongst multiple observers

are their classifications good?

124
Q

2 groups of independent nominal data

A

pearson’s Chi-square test

125
Q

3 or more groups of independent nominal data

A

chi-square test of independence

126
Q

nominal data: 2 or more groups w/ expected cell count of <5

A

fisher’s exact test

127
Q

nominal data - 3 or more groups of independent data – what to do after getting a significant result

A

must run post-hoc to determine which groups are different

multiple chi square tests is not acceptable

Bonferroni test of inequality

128
Q

2 groups of related nominal data

A

McNemar test

129
Q

3 or more groups of related nominal data

A

Cochran

same as chi squared but mathematically factors in concept of paired data

then Bonferroni test of inequality for post-hoc testing

130
Q

key words for paired/related data

A

pre vs post
before vs after
baseline vs end

131
Q

‘prediction’ is a key word for what type of assessment desired?

A

regression testing

132
Q

‘event-occurrence’ or ‘time to event’ are key phrases for what type of desired assessment?

A

survival testing