MGSC 291 Final Exam
- incorrect data
- ignoring the baseline
- arbitrary comparisons
- misleading comparisons - ANS-types of statistical lies
fabrication, meaningless or misinformed data - ANS-incorrect data
a mistake in reasoning, evaluating, remembering, or other cognitive process, often occurring as
a result of holding onto one's preferences and beliefs regardless of contrary information. -
ANS-cognitive bias
people react differently depending on whether choices are presented as losses or gains.
Losses hurt more than gains feel good
- people avoid risk when a positive frame is presented, but seek ricks when a negative frame is
presented
- risk seeking when facing losses
- rick averse when facing gains - ANS-framing effect
losses hurt more than gains feel good - ANS-loss aversion
- de-emphasize, name, or remove the frame
- analyze both ways - ANS-solutions to framing effect
common human tendency to rely too heavily on the first piece of information offered (the anchor)
when making decisions - ANS-anchoring effect
- ask for multiple, independent opinions
- reconstruct multiple answers from scratch
- downplay initial information; work from fundamentals
- use experts - ANS-solutions to the anchoring effect
predictions based on simple statistical scoring are generally more accurate than predictions
based on expert judgement
the false belief in reliability of our own judgement. - ANS-illusion of validity
the belief that if something can be recalled, it must be important.
- people tend to weight their judgements toward more recent info
- It is easier to recall consequent if those consequences are bigger
ie. shark attacks - ANS-availability bias
, the probability of the complement of an event, P(A^c), is equal to one minutes the probability of
the event.
P(A^c) = 1 - P(A) - ANS-complement rule
the probability that event A or B occurs (at least one of the events happens) is:
P(A u B) = P(A) + P(B) - P(A n B) - ANS-addition rule
two events are independent if the occurrence of one event does not affect the probability of the
occurrence of the other event.
P(A n B) = P(A)*P(B) - ANS-multiplication rule
the probability of an event ( A ), given that another ( B ) has already occurred.
P(A n B) / P(B) - ANS-conditional probability
describes the probability of an event, based on prior knowledge of conditions that might be
related to the event. - ANS-Bayes Theorem
order does not matter - ANS-combination
order matters - ANS-permutation
- the events occur randomly over time (or space)
- the average arrival rate remains constant
- the arrivals are independent of each other
- the random variable (x) describes the number of events within the observed time internal. -
ANS-assumptions when using a Poisson to model arrival processes
trend appears in several different groups of data but disappears or reverses when these groups
are combined.
ex. comparisons between hospitals in the Cardiac Care case study
- One hospital was better for both high and low risk patients, but saw more high-risk patients -
ANS-Simpson's paradox
a discrete probability distribution that expresses the probability of a given number of events
occurring in a fixed interval
similar to a binomial; used when n is large
ex. failures of a machine in one month
- the number of calls coming into a helpline in a given minute - ANS-poisson
used to describe discrete random variables - ANS-probability mass function
used to describe continuous random variables - ANS-probability density function
- incorrect data
- ignoring the baseline
- arbitrary comparisons
- misleading comparisons - ANS-types of statistical lies
fabrication, meaningless or misinformed data - ANS-incorrect data
a mistake in reasoning, evaluating, remembering, or other cognitive process, often occurring as
a result of holding onto one's preferences and beliefs regardless of contrary information. -
ANS-cognitive bias
people react differently depending on whether choices are presented as losses or gains.
Losses hurt more than gains feel good
- people avoid risk when a positive frame is presented, but seek ricks when a negative frame is
presented
- risk seeking when facing losses
- rick averse when facing gains - ANS-framing effect
losses hurt more than gains feel good - ANS-loss aversion
- de-emphasize, name, or remove the frame
- analyze both ways - ANS-solutions to framing effect
common human tendency to rely too heavily on the first piece of information offered (the anchor)
when making decisions - ANS-anchoring effect
- ask for multiple, independent opinions
- reconstruct multiple answers from scratch
- downplay initial information; work from fundamentals
- use experts - ANS-solutions to the anchoring effect
predictions based on simple statistical scoring are generally more accurate than predictions
based on expert judgement
the false belief in reliability of our own judgement. - ANS-illusion of validity
the belief that if something can be recalled, it must be important.
- people tend to weight their judgements toward more recent info
- It is easier to recall consequent if those consequences are bigger
ie. shark attacks - ANS-availability bias
, the probability of the complement of an event, P(A^c), is equal to one minutes the probability of
the event.
P(A^c) = 1 - P(A) - ANS-complement rule
the probability that event A or B occurs (at least one of the events happens) is:
P(A u B) = P(A) + P(B) - P(A n B) - ANS-addition rule
two events are independent if the occurrence of one event does not affect the probability of the
occurrence of the other event.
P(A n B) = P(A)*P(B) - ANS-multiplication rule
the probability of an event ( A ), given that another ( B ) has already occurred.
P(A n B) / P(B) - ANS-conditional probability
describes the probability of an event, based on prior knowledge of conditions that might be
related to the event. - ANS-Bayes Theorem
order does not matter - ANS-combination
order matters - ANS-permutation
- the events occur randomly over time (or space)
- the average arrival rate remains constant
- the arrivals are independent of each other
- the random variable (x) describes the number of events within the observed time internal. -
ANS-assumptions when using a Poisson to model arrival processes
trend appears in several different groups of data but disappears or reverses when these groups
are combined.
ex. comparisons between hospitals in the Cardiac Care case study
- One hospital was better for both high and low risk patients, but saw more high-risk patients -
ANS-Simpson's paradox
a discrete probability distribution that expresses the probability of a given number of events
occurring in a fixed interval
similar to a binomial; used when n is large
ex. failures of a machine in one month
- the number of calls coming into a helpline in a given minute - ANS-poisson
used to describe discrete random variables - ANS-probability mass function
used to describe continuous random variables - ANS-probability density function
