Independent
Events
Events
are considered independent events if the occurrence of one has
no bearing on the probability of the occurrence of the other. A good
example to illustrate this is the tossing of a fair coin. A coin
has no memory, meaning that even if a head comes up 10 times in a
row, the probability that the next toss is a head is still ½. The
probability that the next toss is a tail is also ½. The probability
that the toss of a fair coin lands heads up or tails up on any single
toss is always ½ no matter how many times the coin has been
previously tossed.
If
two events A and B are independent, then P(A
∩ B) = P(A) ∙ P(B).
• Note
that the rule for independent events can be extended for more than 2
events. If there are 3 independent events, A, B and C,
then P(A ∩ B ∩ C) = P(A) ∙
P(B) ∙ P(C). Similar results are seen
for 4 events, 5 events and so on.
Example:
Suppose a spinner has 12 regions numbered 1 – 12 each of which
the spinner is equally likely to land on. What is the probability
that the spinner will land on 2, 2, 10, 9 and any odd number in 5
consecutive spins?
P(2)
= 1/12
P(10)
= 1/12
P(9)
= 1/12
P(odd)
= ½
Therefore,
since these are independent events, the probability the spinner lands
on 2, 2, 10, 9 and any odd number in 5 consecutive spins is
(1/12)(1/12)(1/12)(1/12)(1/2)
= 1/41,472