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Epidemiology exam III > Stats > Flashcards

Stats Flashcards

1
Q

Sample

A

a subset or portion of the full population (representatives)

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2
Q

When is a sample useful

A

When studying the complete population is not feasible

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3
Q

What is commonly utilized to draw samples

A

random processes

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4
Q

study measurments

A
  • measurements are collected on desired variables
  • comparisons are made (statistical analyses)
  • Inferences will be made about the sample-derived measurements and their comparisons

(inferences will also be made to the full population of similar subjects (generalizability))

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5
Q

Null hypothesis

A

perspective which states there will be no (true) difference between groups

-most conservative and commonly utilized

  • Various statistical-perspectives can be taken by the researcher
    • superiority
    • Noninferiority
    • Equivalency
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6
Q

Alternative hypothesis

A

Perspective which states there will be a (true) difference between the groups being compared

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7
Q

what are the 2 key attributes of data measurements (variables)

A
  • Magnitude (or Dimensionality)
  • Consistency of scale (or Fixed interval)
    • equal, measurable spacing between units
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8
Q

Study population

A

the final group of individuals selected for a study

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9
Q

What are the 3 key levels and attributes of measurements

A
  • Nominal
  • Ordinal
  • Interval/Ratio
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10
Q

Explain Nominal data

A
  • Dichotomous/Binary; non-ranked Named categories
    • No magnitude/No consistency of scale / No Rational Zero
    • Nominal variables are simply labeled variables without quantitative characteristics
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11
Q

_______ variables are simply labeled variables without quantitative characteristics

A

Nominal

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12
Q

Explain Ordinal Data

A
  • Ranked categories; non-equal distance

- Yes Magnitude/no consistency of scale/ no Rational zero

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13
Q

Interval/Ratio Data

A
  • order and magnitude and equal intervals-of-scale (units)
  • Yes magnitude/ Yes consistency of scale/ no or yes rational zero (no-interval; yes-ratio)
    Ex. - Number of living sibling and personal age (in years)
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14
Q

after data is collected, we can appropriately go _________ specificity/detail of data measurements (levels), but never go ____

A

down, up

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15
Q

What are the two discrete data types

A

nominal and ordinal

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16
Q

What kind of data is continuous

A

interval

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17
Q

Mean/Median/mode are only useful for _____ data

A

continuous

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18
Q

Difference in each individual measurement value and the groups’ mean

A

Variance

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19
Q

Square root of variance value (restores units of mean)

A

Standard Deviation

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20
Q

Stat tests useful for normally-distributed data are called ________ tests

A

Parametric

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21
Q

Asymmetrical distribution with one “tail” longer than another

A

Positively skewed

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22
Q

A distribution is skewed anytime the _____ differs form the _____

A

median differs from the mean

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23
Q

When mean is _____ than median it is _____ skew

A

positive skew

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24
Q

Asymmetrical distribution with one “tail” longer than another

A

Negatively skewed

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25
Q

Distribution is skewed anytime the _____ differs from the _____

A

median differs from the mean

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26
Q

When mean is _____ than the median it is a negative skew

A

lower

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27
Q

A measure of the asymmetry of a distribution

A

Skewness

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28
Q

The perfectly-normal distribution is symmetric and has a skewness value of

A

0

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29
Q

A measurement of the extent to which observations cluster around the mean. For a normal distribution, the value of the kurtosis statistic is 0

A

Kurtosis

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30
Q

Postive kurtosis means ______ clustered

A

More

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31
Q

Negative kurtosis means _____ clustered

A

less

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32
Q

What are the required assumptions of interval data (for proper selection of a parametric test)

A
  • Normally-distributed
  • Equal variances
  • Randomly-derived and independent
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33
Q

Test used to see if there is equal variances between groups

A

Lavene’s test

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34
Q

How to handle interval data that is not normally distributed

A
  • Use of a statistical test that does not require the data to be normally-distributed (non-parametric tests)
  • Or transform data to a standardized value (z-score or log)
    • hoping that the transformation allows data to be normally-distributed
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35
Q

The ability of a study design, its methodology, and the selected test statistic to detect a true difference if one truly exists between group-comparisions (analogous to sensitivity in screening)

A

Power (1-Beta)

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36
Q

The larger the ______, the greater the likelihood (ability) of detecting a difference if one truly exists (increases power)

A

Sample size

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37
Q

sample size determination

A
  • Minimum difference groups deemed significant
    • The smaller the difference between groups necessary to be considered “significant” (important), the greater number needed (“N”)
  • Expected variation of measurement (known or estimated)
  • Alpha (Type 1) and Beta (Type 2) Error Rates (power)
    • add in anticipated drop-outs or loss to follow-ups
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38
Q

P value

A

probability of observing, due to chance alone

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39
Q

The P value is selected by investigators before

A

the study starts (a priori)

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40
Q

Customarily the pre-selected p value is _____. Meaning what?

A
  • customarily 5% (0.05)

- The risk of experiencing a Type 1 error is acceptably low (less than 5%)

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41
Q

Type I error (alpha error)

A
  • False positive

- Rejecting the null hypothesis when it is actually true, and you should have accepted it

42
Q

Type II error (beta error)

A
  • False negative

- Not rejecting the null hypothesis when it is actually false, and you should have rejected it

43
Q

What are 5 different ways to interpret a pre-set p value

A
  • the probability of making a type 1 error if the null hypothesis is rejected
  • The probability of erroneously claiming a difference between groups when one does not really exist
  • The probability of the outcome of the group’s differences occurring by chance
  • The probability of obtaining group differences as great or greater if the groups where actually the same/equal
  • The probability of obtaining a test statistic as high/higher if the groups were actually the same/equal
44
Q

What test do you want to have a high p value and why

A

Levenne’s test

  • because you don’t want there to be a statistical significance among the test groups
45
Q

What are the most common selections for confidence intervals

A

90%, 95%, and 99%

46
Q

The confidence intervals high and low value are calculated at an a priori percentage of confidence that statistically the real (yet unknown) difference or relationship _____

A

resides

47
Q

Confidence intervals are based on

A

-Variation in sample (V/SD) and Sample size (N)

48
Q

If CI crosses 1.0 (for ratio (OR/RR/HR) or 0.0 (for other comparisons (ex. interval variables) than the data is

A

not significant

49
Q

when reviewing a study it is important to ask

A

Does “statistical” significance confer meaningful “clinical” significance

50
Q

What are the 4 key questions in selecting the correct statistical test

A
  1. ) What type of Data is being collected/evaluated
  2. ) What type of comparison/assessment is desired
  3. ) How many groups are being compared
  4. ) is the data independent or related (paired)
51
Q

When asking yourself what typed of dat is bing collected/evaluated you further ask

A
  • does the data have magnitude?

- Does the data have fixed, measurable interval along the entire scale

52
Q

_____ provides a quantitative measure of the strength and direction of a relationship between variables

A

Correlation (r)

53
Q

Value range for correlation (r)

A

from -1.0 to +1.0

54
Q

A correlation that controls for confounding variables is a

A

partial correlation

55
Q

What is the nominal correlation test

A

Contingency coefficient

56
Q

What is the ordinal correlation test

A

Spearman correlation

57
Q

What is the interval correlation test

A

Pearson correlation

58
Q

All correlation tests can be run as a ______ to control for confounding

A

partial correlaiton

59
Q

p>0.05 for a pearson correlation just means there is no _____ correlation; there may still be a _____ correlation present

A

linear, non-linear correlation present

60
Q

What is the nominal proportion of events (survival) test

A

Log-Rank

61
Q

Type of test that compares the proportion of, or time-to, event occurrences between groups

A

Survival tests (proportion of evens)

62
Q

survival tests are commonly represented by a

A

Kaplan-meier curve

63
Q

What is the name of ordinal survival test

A

Cox-Proportional Hazards test

64
Q

What is the name of the interval survival test

A

Kaplan-Meier test

65
Q

Type of test that provides a measure of the relationship between variables by allowing the prediction about the dependent, or outcome, variable (DV) knowing the value/category of independent variables (IV’s)

A

Regressions

66
Q

Regression test are able to calculate ____ for a measure of Association

A

OR

67
Q

Nominal regression test

A

Logistic regression

68
Q

Ordinal regression test

A

Multinomial logistic regression

69
Q

Interval regression test

A

linear regression

70
Q

If the type of comparison/assessment is frequencies/counts/proportions then you

A

must ask questions 3 and 4

  1. ) how many groups are being compared
  2. ) is the data independent or related (paired)
71
Q

What are some buzz words for paired data

A
  • pre vs. post
  • before vs. after
  • beginning vs. end
  • Baseline vs. end
72
Q

what is the test for interval date greater than or equal to 3 groups with independent data

A

ANOVA or MANOVA

73
Q

What is the type of test used for 2 groups of Nominal independent data

A

(Pearson’s) Chi-square test (x^2)

74
Q

What is the type of test used for greater than or equal to 3 groups of independent nominal data

A

chi-squar test of independence (X^2) or Fisher’s exact test

75
Q

What is the type of test used for greater than or equal to 2 groups of nominal data with cell count

A

Fisher’s exact test

76
Q

What are the are the assumption of chi-squared tests

A
  • usual chi-square (binomial) distribution for nominal data

- no cell with expected count

77
Q

For statistically significant finding in 3 or more comparisons study of nominal independent data one must perform_________ to determine which groups are different: multiple chi-squared test are never acceptable, why? and what test is normally used

A
  • subsequent analysis (post-hoc testing)
  • Multiple chi-squared tests are never acceptable because the risk of a type 1 error increases with each additional test (almost guaranteed after 4-5 tests)
  • Bonferroni test of Inequality (Bonferroni correction)
    • adjusts the p value for # of comparisons being made
    • very conservative
78
Q

2 groups of paired/related nominal data use what test

A

McNemar test

79
Q

greater than or equal to 3 groups of paired/related nominal data use what test

A

Cochran (note that if statistically significant than bonferroni test of inequality is used)

80
Q

test for 2 groups of independent ordinal data

A

Mann-Whitney test

81
Q

Test for greater than or equal to 3 groups of ordinal data

A

kruskal-wallis test

82
Q

Mann-whitney and kruskal-wallis test both compare the _____ values between groups

A

median

83
Q

test for 2 groups of paired/related ordinal data

A

Wilcoxon signed rank test

84
Q

Test for greater than or equal to 3 groups of paired/related ordinal data

A

Friedman Test

85
Q

Both the wilcoxon signed rank test and friedman test compare the ____ values between groups

A

median

86
Q

What are the post-hoc tests for 3 or more groups of ordinal data? explain each

A
  • Student-Newman-Keul test
    • Compares all pairwise comparisons possible
    • all groups must be equal size
  • Dunnett test
    • Compares all pairwise comparisons against a single control
    • All groups must be equal in size
  • Dunn test
    • compares all pairwise comparisons possible
    • Useful when all groups are not of equal size
87
Q

which of the post-hoc tests for ordinal data does not require groups to be equal in size

A

Dunn test

88
Q

test for 2 groups of independent interval data

A

Student t-test

89
Q

Test for 3 or more groups of independent interval data

A
  • Analysis of Variance (ANOVA) (note an ANOVA can truly handle 2 or more groups)
  • Multiple Analysis of Variance (MANOVA)
90
Q

Both ANOVA and student t-test compare the ____ of all groups (along with infra- and inter- group variations) against a single Dependent variable

A

means

91
Q

MANOVA compares the means of all groups against ______

A

multiple Dependent variables

92
Q

Test for greater than or equal to 3 groups of independent interval data with cofounders

A
  • Analysis of Co-Varience (ANCOVA)

- Multiple analysis of Co-Variance (MANCOVA)

93
Q

an ANCOVA test compares the means of all groups (along with intra and inter-group variations) against a single DV while also

A

controlling for the co-variance of confounders

94
Q

An MANCOVA test compares the means of all groups (along with intra and inter-group variations) against multiple DVs while also

A

controlling for the co-variance of confounders

95
Q

Test for 2 groups of paired/related interval data

A

Paired t-test

96
Q

When you see mean think what kind of data

A

interval

97
Q

test(s) for greater than or equal to 3 groups of paired/related interval data

A
  • repeated measures ANOVA

- Repeated measures MANOVA

98
Q

Test for greater than or equal to 3 groups of Paired/related interval data with cofounders

A
  • repeated measures ANCOVA

- Repeated measures MANCOVA

99
Q

What are the post-hoc tests for 3 or more groups of interval data? explain them

A
  • Student-Newman-Keul test
    • Compares all pairwise comparisons possible
    • all groups must be equal
  • Dunnett test
    • Compares pairwise comparisons against a single control
    • all groups must be equal in size
  • Dunn test
    • Compares all pairwise comparisons possible
    • Useful when groups are not of equal size
  • Tukey and scheme tests
    • compares all pairwise comparisons possible
    • all groups must be of equal size
    • Tukey test- slightly more conservative than the student-newman-keul test
    • Scheffe test- Less affected by violations in normality and homogeneity of variances-most conservative
  • Bonferroni correction
    • adjusts the p value for # of comparisons being made
      • very conservative
100
Q

if you see predict then think what kind of test

A

Regression

101
Q

agreement between evaluators (consistency of “decisions”, “ determinations” )

A

kappa statistic

102
Q

Kappa interpretations

A

+1= the observers perfectly “classify” everyone exactly the same way

0=there is no relationship at all between the observer’s classifications”, above the agreement that would be exited by chance

  • 1= the observers “classify” everyone exactly the opposite of each other

+1= good agreement

-1= poor agreement

Epidemiology exam III (2 decks)

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