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individuals who have to pay for something and do not receive direct
benefits from it
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1 Casual Inference 2 Random Error
3 forced riders 4 Control Group
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Terms in this set (82)
drawing a conclusion about casual relationships
Casual Inference between various inputs and policy outputs and
outcomes
any change in a subject's performance due to
Repetition Effect
repeating the experimental condition
, we are never able to know how a subject would
Fundamental Problem of
have behaved in the counterfactual because they
Casual Interference
did not, in fact, receive the treatment
consists of subjects who would drive the course
Treatment Group
while texting
consists of subjects who would drive the course
Control Group
without texting
a set of variables that are correlated with both our
Confounding Variable
treatment and our potential outcome
when a relationship between two variables appears
Spurious Relationship to exist, due to the confounding presence of a third
variable
the likelihood or level of confidence that a casual
Internal Validity inference drawn from an analysis reflects the "true"
underlying casual relationship
whether the findings from a study based upon a
External Validity sample or an experiment can be extended or
generalized to a larger population
help describe both the average response and
Descriptive statistics dispersion of opinion around the average for a given
population of stakeholders or clientele
tells us whether and how strongly two variables
Correlation
tend to co-vary
process of estimating a precise expression of
uncertainty in some claim that derives primarily from
Scientific uncertainty
three sources o =f error; measurement error,
random error, and sampling error
the stochastic or random probability that some
Random Error
events are simply not perfectly predictable
entire set of individuals or cases with which an
Population
analyst is interested in examining or learning about
Sample subset of the population
Parameters characteristics of the population