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2-4 CONDITIONAL PROBABILITY 39
Surface flaw
360 40
No Yes
400 400
Defective
Figure 2-13 Tree 342 No Yes 18 30 No Yes 10
360 360 40 40
diagram for parts
classified
Therefore, P1B ƒ A2 can be interpreted as the relative frequency of event B among the trials that
produce an outcome in event A.
EXAMPLE 2-17 Again consider the 400 parts in Table 2-3. From this table
10 40 10
P1D ƒ F2 P1D ¨ F2
P1F2 ^
400 400 40
Note that in this example all four of the following probabilities are different:
P1F2 40
400 P1F ƒ D2 10
28
P1D2 28
400 P1D ƒ F2 10
40
Here, P(D) and P1D ƒ F2 are probabilities of the same event, but they are computed under two
different states of knowledge. Similarly, P(F) and P1F ƒ D2 are computed under two different
states of knowledge.
The tree diagram in Fig. 2-13 can also be used to display conditional probabilities. The
first branch is on surface flaw. Of the 40 parts with surface flaws, 10 are functionally defec-
tive and 30 are not. Therefore,
P1D ƒ F2 10
40 and P1D¿ƒ F2 30
40
Of the 360 parts without surface flaws, 18 are functionally defective and 342 are not. Therefore,
P1D ƒ F¿2 342
360 and P1D¿ƒ F¿2 18
360
Random Samples from a Batch
Recall that to select one item randomly from a batch implies that each item is equally likely.
If more than one item is selected, randomly implies that each element of the sample space is
equally likely. For example, when sample spaces were presented earlier in this chapter, sam-
pling with and without replacement were defined and illustrated for the simple case of a batch
with three items {a, b, c}. If two items are selected randomly from this batch without replace-
ment, each of the six outcomes in the ordered sample space
S without 5ab, ac, ba, bc, ca, cb6
has probability 1
6 . If the unordered sample space is used, each of the three outcomes in
{{a, b}, {a, c}, {b, c}} has probability 1
3 .