Mutually exclusive events Working my way through the following problem:
Problem

Suppose that $E$ and $F$ are mutually exclusive events of an
experiment.  Show that if independent trials of this experiment are
performed,  then $E$ will occur before $F$ with probability
$\frac{ P( E)}{P( E) + P( F)}.$

I have the following come up with the following solution:
Solution

Since
$P( E^c) = P( F)$
Therefore
$\frac{ P( E)}{ P( E) + P( F)} = \frac{ P( E)}{ 1 - P( F) + P( F)} = \frac{ P( E)}{ 1} = P( E)$

But I am unsure if I am able to assume $P( E^c) = P( F)$ as a given?
As well, I am particularly confused by the answer in the solution manual which makes it's argument as follows:
Solution Manual

If $E$ and $F$ are mutually exclusive events in an experiment,  then
$P( E \cup F) = P( E) + P( F)$. We desire to compute the probability
that $E$ occurs before $F$ , which we will denote by $p$. To compute
$p$ we condition on the three mutually exclusive events $E$, $F$ , or
$(E \cup F )^c$. This last event are all the outcomes not in $E$ or
$F$. Letting the event $A$ be the event that $E$ occurs before $F$, we
have that
$p = P( A|E) P( E) + P( A|F) P(F ) + P( A|(E \cup F )^c) P( (E \cup F )^c)$
$P( A|E) = 1$
$P( A|F) = 0$
$P( A|(E \cup F)^c) = p$
since if neither $E$ or $F$ happen the next experiment will have $E$
before $F$ (and thus event $A$ with probability $p$). Thus we have
that
$p = P( E) + p P( (E \cup F)^c)$
$= P( E) + p (1 − P( E \cup F))$
$= P( E) + p (1 − P( E) − P( F))$
Solving for $p$ gives
$p = \frac{ P( E)}{ P( E) + P( F)}$
as we were to show.

Specifically his statement

since if neither $E$ or $F$ happen the next experiment will have $E$
before $F$ (and thus event $A$ with probability $p$)

 A: Your solution is incorrect. If $E$ and $F$ are mutually exclusive, it means that $E \cap F = \emptyset$, therefore $F \subseteq E^c$; and therefore, $P(F) \color{red}{\le} P(E^c)$.
You are not interpreting independent trials of the experiment correctly. It might be helpful to consider an example. Suppose you are rolling a biased 6-faced die. Let $E$ denote the event that 1 or 2 turn up and $F$ denote the event that 3 or 4 turn up. Continue rolling the die until either $E$ or $F$ occur. What is the probability that $E$ occurs before $F$, that is what is the probability that you get 1 or 2 before you get 3 or 4 (in the repeated rolls of the die).
The statement

since if neither $E$ or $F$ happen the next experiment will have $E$ before
$F$ (and thus event $A$ with probability $p$)

means that if neither $E$ or $F$ happen, that is if 5 or 6 is rolled, we roll the die again. Since the rolls are independent, the probability of getting $E$ before $F$ in the future experiments is $p$.
Rant: This problem and its solution shows why students find probability confusing. The problem is stated very informally. In my opinion, a formal statement of the problem will remove some of the confuson.
Consider an experiment $\mathcal E_1$ with probability measure $P_1$. Let $E$ and $F$ be two events in $\mathcal E_1$. $P_1(E)$ denotes the probability that $E$ occurs in experiment $\mathcal E_1$. Similarly interpretation holds for $P_1(F)$.
Now consider another experiment $\mathcal E_2$, which represents infinite independent repetitions of the experiment $\mathcal E_1$. Let $P_2$ be the probability measure for events in $\mathcal E_2$.
Now consider an outcome $\omega$ of $\mathcal E_2$ that is a series of outcomes of $\mathcal E_1$. Let $\tau_E$ denote the first time $E$ occurs in $\omega$ (with $\tau_E = \infty$ if $E$ does not occur). Similarly, let $\tau_F$ denotes the first time $F$ occurs in $\omega$.  Let $A$ denote the event (in $\mathcal E_2$) that $\tau_E < \tau_F$. The question is asking you to show that

$\displaystyle  P_{\color{red}2}(A) = \frac{ P_1(E) }{ P_1(E) + P_1(F) }$

Thus, the question is asking you to compare two different experiments. When you write $E^c \equiv F$, you were thinking in terms of experiment $\mathcal E_2$; but $E$ and $F$ are not events in $\mathcal E_2$; they are events in $\mathcal E_1$.
A: To determine the probability that $E$ occurs before $F$, we can ignore
all the (independent) trials on which neither $E$ nor $F$ occurred,
that is, $(E\cup F)^c$ occurred, since we are going to repeat the
experiment until one of $E$ and $F$ does occur.  So, look at the
trial of the experiment on which one of $E$ and $F$ has occurred
for the very first time.
We are given that on this trial, the event $E \cup F$ has occurred.
But, we don't yet know which of the two has occurred. So, given the
knowledge that $E \cup F$ has occurred, what is the conditional
probability that it was $E$ that occurred (and so $E$ occurred before $F$
since this is the first time we have seen either $E$ or $F$)?
$$P(E \mid (E \cup F)) = \frac{P(E(E \cup F))}{P(E \cup F)}
= \frac{P(E \cup EF)}{P(E) + P(F) - P(EF)} 
= \frac{P(E)}{P(E)+P(F)}$$
since $P(EF) = P(\emptyset) = 0$.

Alternatively, let $G = (E\cup F)^c = E^c \cap F^c$ be the event that neither
$E$ nor $F$ occurs on a trial of the experiment. Note that 
$P(G) = 1 - P(E) - P(F)$.  Then, the event $E$ occurs
before $F$ if and only if one of the following compound events occurs:
$$
E, (G, E), (G, G, E), \ldots, (\underbrace{G, G, \ldots, G,}_{n-1} E), \ldots
$$
where $(\underbrace{G, G, \ldots, G,}_{n-1} E)$ means $n-1$ trials on which $G$
occurred and then $E$ occurred on the $n$-th trial.  The desired probability
is thus
$$P(E ~\text{before}~ F) = P(E) + P(G)P(E) + [P(G)]^2P(E) + \cdots
= \frac{P(E)}{1 - P(G)} = \frac{P(E)}{P(E)+P(F)}.$$
A: I think extreme simplification is need...
$P(E) and P(F)$ are complements for the Universe (U, U=1 in this case)
$P(E) + P(F) = 1$  //  corrected as mentioned by Aditya, sorry for my dyslexic!thing
$P(E) / ( P(E)+P(F) ) = 1 / 2$ Hence 
    probability of $E$ is $50\%$ (or $0.5$), 
    probability of restant set is the remaining $50\%$; 
    the remaining set is $F$ because $U=\{E, F\}$
    with the given data $P(E \text{ before } F) = P(F \text{ before }E)$.
A: Here is an alternative way of using conditional probability.
Consider repeated experiments and let $Z_n$ ($n \in \mathbb{N}$) be the result observed on the $n$-th experiment.
Denote the event of "$\textrm{E before F}$" by $B$ and its probability $\alpha$. 
According to the law of total probability, we obtain
$$\alpha = P \{ B\} = \sum_{z} P \{B \mid Z_1 = z \} P \{ Z_1 = z \}$$
Now we have
$$P \{ B \mid Z_1 = E \} = 1, \quad P \{ B \mid Z_1 = F \} = 0.$$
If the first experiment results in anything other than $E$ or $F$, the problem is repeated in a statistically identical setting. That is, 
$$P \{ B \mid Z_1 = z \} = \alpha, \forall z \neq E, F.$$
Therefore, we have
$$\alpha = P \{ Z_1 = E \} \times 1 + P \{ Z_1 = F \} \times 0 + \sum_{z \neq E,F} P \{ Z_1 = z \} \times \alpha \\ = P \{ Z_1 = E \} + [1 - P \{ Z_1 = E \} - P \{ Z_1 = F \}] \alpha$$
Solving this equation, we get
$$\alpha = \frac{P \{ Z_1 = E \}}{P \{ Z_1 = E \} + P \{ Z_1 = F \}}.$$
