Variables
the characteristics of the individuals within the population
Key Point
Variables vary
Qualitative or Categorical variables
allow for classification of individuals based on some attribute or characteristic
Quantitative variables
provide numerical measures of individuals
Discrete variable
has either a finite number of possible values or a countable number of possible values
continuous variable
has an infinite number of possible values that are not countable
data
a list of observations a variable assumes
cofounding variable
occurs when two or more variables are not separated
lurking sample
an explanatory variable that is not considered/left out in the study
sampling error
results in an estimate for the population
nonsampling error
result from bias in human conducted surveys
cross-sectional study
collect information over periods of time and compares
Case-control study
are retrospective and cause you to look back in time
cohort study
are prospective to look forward in time
data-entry error
leads to results that are not representative of the population
census
list of total population count
response bias
when you lie about data after recording a survey
webscraping or data mining
when you extract data from the inner web
nonresponse bias
when people do not reply to a survey but have an opinion
cluster sample
people are separated by characteristics and picked by random
convience sample
only members of a population who are easy to reach are selected
sampling bias
a favor in play when surveyed
undercoverage
Results in sampling bias. Occurs when the proportion of one segment of the population is lower in a sample than it is in the population.