Biostats chapter three
Terms
undefined, object
copy deck
 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
 formal addition rule
 P(A or B)=P(A)+P(B)P(A and B)
 intuitive addition rule
 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)=1P(A)
P(A)=1P(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