Probability of Independent Events Not Happening
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Theorem
Let $\mathcal E = \left({\Omega, \Sigma, \Pr}\right)$ be a probability space.
Let $A_1, A_2, \ldots, A_m \in \Sigma$ be independent events in the event space of $\mathcal E$.
Then the probability of none of $A_1$ to $A_m$ occurring is:
- $\displaystyle \prod_{i=1}^m \left({1 - \Pr \left({A_i}\right)}\right)$
Corollary
Let $A$ be an event in an event space of an experiment $\mathcal E$ whose probability space is $\left({\Omega, \Sigma, \Pr}\right)$.
Let $\Pr \left({A}\right) = p$.
Suppose that the nature of $\mathcal E$ is that its outcome is independent of previous trials of $\mathcal E$.
Then the probability that $A$ does not occur during the course of $m$ trials of $\mathcal E$ is $\left({1 - p}\right)^m$.
Proof
Let $A_1, A_2, \ldots, A_m \in \Sigma$ be independent events.
From Independent Events are Independent of Complement, we have that $\Omega \setminus A_1, \Omega \setminus A_2, \ldots, \Omega \setminus A_m \in \Sigma$ are also independent.
From the definition of occurrence, if $A$ does not happen then $\Omega \setminus A$ does happen.
So for none of $A_1, A_2, \ldots, A_m$ to happen, all of $\Omega \setminus A_1, \Omega \setminus A_2, \ldots, \Omega \setminus A_m$ must happen.
From Elementary Properties of Probability Measure:
- $\forall A \in \Omega: \Pr \left({\Omega \setminus A}\right) = 1 - \Pr \left({A}\right)$
So the probability of none of $A_1$ to $A_m$ occurring is:
- $\displaystyle \prod_{i=1}^m \left({1 - \Pr \left({A_i}\right)}\right)$
$\blacksquare$
Proof of Corollary
It can immediately be seen that this is an instance of the main result with all of $A_1, A_2, \ldots, A_m$ being instances of $A$.
The result follows directly.
$\blacksquare$
Sources
- Geoffrey Grimmett and Dominic Welsh: Probability: An Introduction (1986): $\S 1.7$: Exercise $24 \ \text{(i)}$