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Epidemiology Final Exam > Intro to Biostats lecture 29-34 > Flashcards

Intro to Biostats lecture 29-34 Flashcards

1
Q

Study Subjects:

Sample

A
  • a subset or portion of the full population “representatives”
  • useful when studying the complete population is not feasible
  • random processes commonly utilized to draw a sample
  • hope that info generated from sample is generalizable to entire population
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2
Q

Study Measurements

A
  • data will be collected on desired variables
  • dependent variables= outcome variables
  • independent variables
  • comparisons= statistical analyses
  • inferences= made about measurements and their comparisons in relation to the null (also be made about full population of similar subjects= generalizability)
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3
Q

Null Hypothesis (Ho)

A
  • researchers either accept or reject based on data analysis
  • states there will be no true difference between groups being compared
  • most conserved/commonly used
  • perspectives: superiority, non-inferiority, equivalency
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4
Q

Alternative Hypothesis (H1)

A

-a research perspective which states there will be a true difference between the groups being compared

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5
Q

3 attributes of data measurement:

A
  1. magnitude
  2. consistency of scale
  3. rational/absolute zero
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6
Q
  1. Magnitude
A
  • dimensionality
  • bigger/taller/shorter/stronger
  • does the data have magnitude?
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7
Q
  1. Consistency of Scale
A
  • fixed interval
  • equal, measurable spacing between units
  • height= inches, feet, cm
  • does the measurement being acquired have consistency of scale?
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8
Q

3 categories for data based on the 2 key attributes:

A
  1. nominal
  2. ordinal
  3. interval/ratio
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9
Q
  1. Nominal
A
  • dichotomous/binary
  • non-ranked, named categories
  • no magnitude/ no consistency of scale/ no rational zero
  • labeled variables w/o quantitative characteristics
  • all data pushed into 2 categories
  • ex: gender? hair color? occupation?
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10
Q

Study Subjects:

Population

A
  • all individuals

- not to be confused with “study population”

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11
Q
  1. Ordinal
A
  • ranked categories, non-equal distance
  • yes magnitude/ no consistency of scale/ no rational zero
  • all pain scales (even numbers are ordinal because numbers are place holders)
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12
Q
  1. Interval/Ratio
A
  • order and magnitude and equal intervals of scale (units)
  • yes magnitude/ yes consistency of scale
  • no rational zero= interval
  • yes rational zero= ratio

ex) number of living siblings, personal age, blood sugar, labs

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13
Q

how can you change levels of measurement data?

A

-after data is collected we can appropriately go down in specificity/detail of data measurement but never go up!
ratio>interval>ordinal>nominal
*can make interval ordinal/nominal but not vice versa

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14
Q

what are descrete variables?

A
  • categorical in nature

- nominal and ordinal

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15
Q

what are continuous variables?

A
  • have scale

- interval/ratio

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16
Q

what are the measures of central tendency

A
  • mean/median/mode
  • min/max/range
  • interquartile range
  • mode useful for nominal/ordinal/interval data
  • mean/median/ range useful for interval only
17
Q

what is variance and standard deviation?

A
  • variance= differences in each individual measurement value and the groups mean
  • interpretation of the spread of the data, tells us how uniform or not the data is

-SD= square root of variance value (restores units of mean)

18
Q

Normally Distributed data

A
  • symmetrical shape
  • parametric tests most useful
  • mean/median/mode are equal or near equal
  • equal dispersion of curve tails to both sides of the mean/med/mode
19
Q

what is the % of the population compared in 1, 2, 3 standard deviations?

A

1 SD= 68%
2 SD= 95%
3 SD= 99%

20
Q

Positively Skewed data

A
  • asymmetrical w/ tail pointing to the right
  • skewed anytime the median differs from the mean
  • when the mean higher than the median
21
Q

Negatively Skewed data

A
  • asymmetrical distribution with one tail pointing toward the left
  • distribution is skewed anytime the median differs from the mean
  • when the mean is lower than the median
22
Q

what is skewness?

A
  • a measure of the asymmetry of a distribution
  • perfectly normal distribution is symmetric and has a skewness value of 0
  • number closest to 0 is normally distributed and as you move away from 0 (3,4,8,20) seeing more skewness
23
Q

what is kurtosis?

A
  • a measure of the extent to which observations cluster around the mean
  • normal distribution the value is 0 (near 0)
  • positive kurtosis= more cluster
  • negative kurtosis= less cluster

*discrete data usually has positive kurtosis because more cluster around

24
Q

what are required assumptions of interval data?

A
  • for proper selection of a parametric test data must be
    1. normally distributed
    2. equal variances
    3. randomly derived and independent
25
Q

how can we tell if variances are equal?

A
  • use levene’s test

- in SPSS test to see if variances are equal

26
Q

how do we handle interval data that is not normally distributed?

A
  1. use a statistical test that does not require the data to be normally distributed (non parametric tests)- treat as if ordinal or nominal
  2. transform data to a standardized value (z-score or log) and hope that makes it normally distributed

**always run descriptive stats and graphs to see if normal distribution

27
Q

what is Power?

A

1-beta

  • ability of the study design and selected stats test to detect a true difference if one truly exists between group comparisons and therefore is the level of accuracy in correctly accepting/rejecting the null
  • ability to find the difference if there is one
28
Q

how does sample size affect power?

A
  • the large the sample size the greater the likelihood of detecting a difference if one truly exists
  • increase sample size= increase power
29
Q

what determines sample size?

A
  1. minimum difference between groups deemed significant
    - smaller the difference between groups necessary to be significant the greater the number needed
  2. expected variation of measurement (known or estimated)
  3. alpha and beta error rates (power)
30
Q

Null Hypothesis (H0)

A
  • a research perspective which states the will be no true difference between the groups being compared
  • most conservative and commonly utilized
  • can take various perspectives (superior, noninferiority, equivalency)
31
Q

Alternative Hypothesis (H1)

A

-a research perspective which states there will be a true difference between the groups being compared

32
Q

different ways to explain statistical significance

P value interpretation

A
  • the probability of making a type 1 error if the null is rejected
  • the probability of erroneously claiming a difference between groups when one does not really exist
  • the probability of the outcome of the groups differences occurring by chance

Epidemiology Final Exam (2 decks)

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