# Biostats chapter three

## Terms

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false positive
test incorrectly indicates the presence of a condition when the subject does not actually have that condition
false negative
test incorrectly indicates that the subject does not have a condition when the subject actually does have that condition
true positive
test correctly indicates that a condition is present when it really is present
true negative
test correctly indicates that a condition is not present when it really is not present
test sensitivity
the probability of a true positive
test specificity
the probability of true negative
positive predictive value
probability that the subject is a true positive given that the test yields a positive result
negative predictive value
probability that the subject is a true negative given the test yields a negative result
prevalence
proportion of subjects having some condition
rare event rule for inferential statistics
if, under a given assumption, the probability of a particular observed event is extremely small, we conclude that the assumption is probably not correct
event
any collection of results or outcomes of a procedure
simple event
an outcome or an event that cannot be further broken down into simpler components
sample space
for a procedure consists of all possible simple events, all outcomes that cannot be broken down any further
rule one: relative frequency approximation of probability
p(A)=number of times A occurred/number of times trial was repeated
rule two: classical approach to probability
-requires equally likely outcomes
P(A)=number of ways A can occur/number of different simple events=s/n
rule three:subjective probabilities
P(A) is estimated by using knowledge of the relevant circumstances
law of large numbers
as a procedure is repeated again and again, the relative frequency probability of an event tends to approach the actual probability
simulation
process that behaves inthe same ways as the procedure itself, so that similar results are produced
complement
of event A, denoted by A bar, consists ofa ll outcomes in which event A does not occur
rounding off probabilities
when expressing the value of a probability, either give the exact fraction or decimal or round off the final decimal results to three significant digits
compound event
any event combining two or more simple events
P(A or B)=P(A)+P(B)-P(A and B)
to find P(A or B), find the sum of the number of ways event A can occur and the number of ways that event B can occur, adding in such a way that every outcome is counted only once. P(A or B) is equal to that sum, divided by the total number of outcomes in the sample space
disjoint
-mutually exclusive
-events A and B cannot occur at the same time
rule of complementary events
P(A)+P(Abar)=1
P(Abar)=1-P(A)
P(A)=1-P(Abar)
tree digram
picture of the possible outcomes of a procedure, shown as line segments emanating from one starting point
independent
two events a and b are independent if the occurence of one does nto affect the probability of the occurence of the other
dependent
if a and b are not independent they are said to be dependent
formal multiplication rule
P(A and B)=P(A)*P(B/A)
intuitive multiplication rule
when finding the probability that event A occurs in one trial and event B occurs in the next trial, multiply the probability of event A by the probability of event B, but be sure that the probability of event B takes into account that previous occurence of event A

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