Expected numbers of boys in a family

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Given $2000$ families with $4$ children in each family and suppose each sex is equally likely, what is the expected numbers of families having at least one boy?

Hint:

One friend tell me that $X$ is the number of families with at least one boy.
$p=\frac{1}{2}$

$$E(X)=\frac{1}{2}\times2000+\left(\frac{1}{2}\right)^2\times2000+\left(\frac{1}{2}\right)^3\times2000+\left(\frac{1}{2}\right)^4\times2000 =1875$$

Anyone can explain to me why the number expected of families with $i$ boys is $2000\times p^i$?

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I think your "friend's" answer might be a fluke. This is because $p = 1/2$ by itself means nothing. If we assume that $p$ is the probability that a family has at least one boy, then this is wrong. The probability that family has at least one boy is $$p = 1-P(\text{A family has no boys}) = 1-\left(\frac12\right)^4 = \frac{15}{16}.$$

Using the method of indicators, I will define $$I_j = \begin{cases} 1, &\text{if $j$th family has at least one boy.}\\ 0, &\text{otherwise.}\end{cases}$$

Recall that $$E[I_j] = P(I_j) = \frac{15}{16}.$$

Finally, I will define $X$ to be the number of families that have at least one boy: $$X = I_1+I_2+\dotsb+I_{2000}.$$

Hence, using the linearity of expectation we have $$E[X] = E[I_1]+\dotsb+E[I_{2000}] = 2000\cdot\frac{15}{16} = 1875.$$

Another way to do this is to calculate $P(I_j)$ directly. You can either recognize that the number of boys a family has follows a binomial distribution, or you can use your preferred counting method, and derive the table: \begin{array}{r|ccccc} k&0&1&2&3&4\\\hline p_k&1/16&4/16&6/16&4/16&1/16 \end{array}

where $p_k$ is the probability family has $k$ boys

Notice that the expectation of $I_j$ is \begin{align*} E[I_j] &= P(I_j) \\ &= \sum_{k = 1}^4 p_k \\ &= \color{red}{\frac{4}{16}+\frac{6}{16}+\frac{4}{16}+\frac{1}{16}}\\ &= \frac{8}{16}+\frac{4}{16}+\frac{2}{16}+\frac{1}{16}\\ &= \frac{1}{2}+\frac{1}{4}+\frac{1}{8}+\frac{1}{16}\\ &=\color{red}{\left(\frac{1}{2}\right)+\left(\frac{1}{2}\right)^2+\left(\frac{1}{2}\right)^3+\left(\frac{1}{2}\right)^4}\\ &=\frac{15}{16}. \end{align*}

We will have \begin{align*} E[X] &= E[I_1]+\dotsb+E[I_{2000}] \\ &= 2000E[I_1]\\ &=2000\color{red}{\left(\frac{1}{2}\right)}+2000\color{red}{\left(\frac{1}{2}\right)^2}+2000\color{red}{\left(\frac{1}{2}\right)^3}+2000\color{red}{\left(\frac{1}{2}\right)^4}\\ &= 2000\cdot\frac{15}{16} \\ &= 1875. \end{align*}

So unless your "friend" was able to cleverly see the relationship in $\color{red}{\text{red}}$, I would say your "friend" got it right for the wrong reasons.

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The probability that a family has at least $1$ boy is $1 - \frac{1}{16} = \frac{15}{16}.$ Thus, the expected number of families that have at least $1$ boy is just $\frac{15}{16} \times 2000 = \boxed{1875}.$

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For $i=1,\dots,2000$ let $X_i$ take value $1$ if family $i$ has at least one boy and let $X_i$ take value $0$ otherwise.

If $X$ denotes the number of families having at least one boy then: $$X=X_1+\cdots+X_{2000}$$ Combining linearity of expectation with the fact that all $X_i$ have equal distribution (hence expectation) we find: $$\mathbb EX=\mathbb EX_1+\cdots+\mathbb EX_{2000}=2000\mathbb EX_1=2000\Pr(X_1=1)$$

Can your find $\Pr(X_1=1)$ yourself? The event $\{X_1=1\}$ is the complement of the event that all $4$ children in family $1$ are girls, so...

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It's because for each family, the probability of having at least one boy is equal to the probability that they are not ALL GIRLS.

The probability of having all girls is the probability the first, 2nd, 3rd and 4th children were all girls, with is $2\times 2\times 2\times 2=2^4$.

So the probability of that not happening is $$1-\frac{1}{2^4}=\frac{15}{16}$$.

Spelling it out more explicitly there are 16 ways of having 4 children; they can go {g,g,g,g}, {g,g,g,b}, {g,g,b,g}, {g,g,b,b}, {g,b,g,g}, {g,b,g,b}, {g,b,b,g}, {g,b,b,b}, {b,g,g,g}, {b,g,g,b}, {b,g,b,g}, {b,g,b,b}, {b,b,g,g}, {b,b,g,b}, {b,b,b,g} or {b,b,b,b}. Fifteen of those have at least one boy.

Then extrapolating across 2000 families, you multiply $$\frac{15}{16}\times2000$$

There is a touch more to this in that this is a binomial distribution and we're relying on the fact that in such a case the mean multiplied by the number of samples is an unbiased estimator of the expected number of successful outcomes.