Prelim #1 Flashcards

1
Q

Random Sample

A

each member of the population has an equal and independent chance of being selected

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2
Q

Volunteer bias

A

volunteers for a study are likely to be different, on average, from the population

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3
Q

sample of convenience

A

a collection of individuals that happen to be available at the time

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4
Q

accuracy

A

an estimate is accurate (unbiased) if the average of estimates is centered on the true population value

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5
Q

precision

A

a measure of how far apart repeated estimates might be, directly related to sample size

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6
Q

experimental study

A

researcher randomly assigns individuals to treatment groups

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7
Q

observational

A

assignment of treatments is not made by researcher

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8
Q

categorical variables

A

describe membership in category/group. could be dichotomous (binary), ordinal (categories are ordered), or nominal (categories have no natural ordering)

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9
Q

numerical/quantitative variables

A

quantitative measurements that have magnitude on a numerical scale. either continuous (can be measured) or discrete (can be counted, individual units)

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10
Q

graphing: 1 categorical variable

A

frequency table, bar graph/bar plot

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11
Q

graphing 1 numerical variable

A

histogram

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12
Q

graphing 2 categorical variables

A

grouped bar graph, mosaic plot

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13
Q

graphing 2 numerical variables

A

scatterplot, line graph (time)

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14
Q

graphing 2 variables: one categorical and one numerical

A

strip chart, side-by-side box plot, multiple histograms

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15
Q

mean vs median

A

mean is the center of gravity, median is middle measurement… mean is more affected by outliers/extreme values than median

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16
Q

sample range

A

max-min…poor measure of distribution width…biased estimator of true range of the population

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17
Q

sample variance (s^2)

A

the average squared difference from the mean….(standard deviation)^2

18
Q

standard (s)

A

positive square root of variance…it is related to the average distance between the mean and each observation…measure of the variability (or spread) of a distribution

19
Q

what percent of data falls within 1 sd of mean (normal distribution)?

20
Q

what percent of data falls within 2 sd of mean (normal distribution)?

21
Q

interquartile range

A

3rd quartile-1st quartile

22
Q

skew

A

measure of asymmetry…refers to pointy tail of a distribution

23
Q

box plot…where do whiskers extend to?

A

largest and smallest non-extreme observation

24
Q

standard error

A

the standard error of an estimator is the standard deviation of the estimator’s sampling distribution…measures precision

25
standard error formula
se=s/square root (n)
26
formula for rough confidence interval and assumptions
sample mean +/ 2SE | normally distributed population and sufficiently large sample size, random sample
27
does confidence interval contain sample mean or true population mean?
true population mean
28
standard errors (increase/decrease) with increasing sample size?
decrease (large samples yield more precise estimates)
29
will a 99% confidence interval be wider or narrower than a 95% confidence interval?
wider
30
will a 90% confidence interval be wider or narrower than a 95% confidence interval?
narrower
31
pseudo-replication
the error that occurs when samples are not independent, but are treated as though they are ex: multiple heart rates measured from the same person and treated as different samples
32
mutually exclusive
two events are mutually exclusive if they cannot both be true... Pr(A and B)=0
33
addition principle
if two events A and B are mutually exclusive, then Pr(A or B)=Pr(A)+Pr(B)
34
general addition principle
A and B don't have to be mutually exclusive. | Pr(A or B)=Pr(A)+Pr(B)-Pr(A and B)
35
probability distributions: discrete
probability is measured by the height of the bar
36
probability distributions: continuous
probability is quantified by area under the curve
37
independence
two events are independent if the occurrence of one gives no information about whether the second will occur
38
multiplication principle
if two events A and B are independent, then Pr(A and B) =Pr(A) X Pr(B)
39
Conditional probability
The conditional probability of an event is the probability of that event occurring given that a condition is met
40
Positive Predictive Value
the probability that subjects with a positive screening test actually have the disease pr(d|+)