Biostatistics Test #1 (Chapters 1-
3)
cases/units - - subjects or objects we obtain information about
- variable - - any characteristic recorded for each case
- categorical variable - - divides cases into groups
- quantitative variable - - measures or records a numerical quantity for each
case
- explanatory variable - - helps explain the response variable
- response variable - - responds to the explanatory variable
- population - - all individuals or objects of interest
- sample - - subset of the population
- sampling bias - - method of selecting causes the sample to differ from the
population in some relevant way; cannot trust generalizations because of
this
- statistical inference - - how to use the information in a sample to make
reliable statements/gain information about the population
- representative sample - - resembles the population, only in smaller
numbers
- simple random sample - - all groups have the same chance of becoming
the sample; each unit of the population has an equal chance of being
selected, regardless of the other units chosen for the sample -- avoids bias
- how to select a random sample - - not haphazardly (picking on our own) -
use a formal method
- bias - - exists when the method of collecting data causes the sample data
to inaccurately reflect the population
- how bias can happen - - 1. wording of questions
2. when we select who to be in sample
use common sense to identify bias
, - association - - values of one variable tend to be related to the values of
the other variable
- causation - - changing the value of one variable influences the value of the
other variable
- confounding variable (factor/lurking variable) - - third variable that is
associated with both the explanatory and response variable
plausible explanation for an association between two variables of interest
- observational study - - data collected with no effort or ability to
manipulate the variables of interest
- (statistical) experiment - - intentionally controlling one or more of the
explanatory variables when producing the data to see how the response
variable changes
- randomized experiment - - value of the explanatory variable for each unit
is determined randomly, before the response variable is measured
- randomized comparative experiment - - randomly assigning cases to
different treatment groups and then comparing the results on the response
variable
- matched pairs experiment - - each case gets both treatments in a random
order and then we examine individual differences in the response variable
between two treatments
- proportion/relative frequencies - - number in that category divided by total
number
- proportion for a sample (symbol) - - p-hat
- proportion for a population (symbol) - - p
- two-way table - - used to show the relationship between two categorical
variables
- outlier - - observed value that is notably distinct from the other values in a
dataset; usually much larger or smaller than the rest of the data
> Q1 - 1.5IQR
< Q3 + 1.5IQR
3)
cases/units - - subjects or objects we obtain information about
- variable - - any characteristic recorded for each case
- categorical variable - - divides cases into groups
- quantitative variable - - measures or records a numerical quantity for each
case
- explanatory variable - - helps explain the response variable
- response variable - - responds to the explanatory variable
- population - - all individuals or objects of interest
- sample - - subset of the population
- sampling bias - - method of selecting causes the sample to differ from the
population in some relevant way; cannot trust generalizations because of
this
- statistical inference - - how to use the information in a sample to make
reliable statements/gain information about the population
- representative sample - - resembles the population, only in smaller
numbers
- simple random sample - - all groups have the same chance of becoming
the sample; each unit of the population has an equal chance of being
selected, regardless of the other units chosen for the sample -- avoids bias
- how to select a random sample - - not haphazardly (picking on our own) -
use a formal method
- bias - - exists when the method of collecting data causes the sample data
to inaccurately reflect the population
- how bias can happen - - 1. wording of questions
2. when we select who to be in sample
use common sense to identify bias
, - association - - values of one variable tend to be related to the values of
the other variable
- causation - - changing the value of one variable influences the value of the
other variable
- confounding variable (factor/lurking variable) - - third variable that is
associated with both the explanatory and response variable
plausible explanation for an association between two variables of interest
- observational study - - data collected with no effort or ability to
manipulate the variables of interest
- (statistical) experiment - - intentionally controlling one or more of the
explanatory variables when producing the data to see how the response
variable changes
- randomized experiment - - value of the explanatory variable for each unit
is determined randomly, before the response variable is measured
- randomized comparative experiment - - randomly assigning cases to
different treatment groups and then comparing the results on the response
variable
- matched pairs experiment - - each case gets both treatments in a random
order and then we examine individual differences in the response variable
between two treatments
- proportion/relative frequencies - - number in that category divided by total
number
- proportion for a sample (symbol) - - p-hat
- proportion for a population (symbol) - - p
- two-way table - - used to show the relationship between two categorical
variables
- outlier - - observed value that is notably distinct from the other values in a
dataset; usually much larger or smaller than the rest of the data
> Q1 - 1.5IQR
< Q3 + 1.5IQR
