To start, let's calculate the mean of the binomial distribution, We expect of course, to find \(\langle \hat{A} \rangle = Np_A\). According to our formula,

\[ \begin{align} \langle \hat{A} \rangle &= f'(1) \\ &= \frac{\partial}{\partial z} \left(p_Az+p_B\right)^N\bigg|_{z=1} \\ &= N p_A \left(p_A+p_B\right)^{N-1} \\ &= N p_A \end{align} \]

And, so, the sample mean, \(\langle\hat{A}\rangle\) is given by \(Np_A\). So far, so good.

Now let's find the sample variance. For this, we'll need the additional piece \(f''(1)\):

\[ \begin{align} f''(z) &= \frac{\partial^2}{\partial z^2} \left(p_Az+p_B\right)^N \\ &= Np_A\frac{\partial}{\partial z} \left(p_Az+p_B\right)^{N-1} \\ &= N(N-1)p_A^2 \left(p_Az+p_B\right)^{N-2} \\ f''(1) &= N(N-1)p_A^2 \left(p_Az+p_B\right)^{N-2}\bigg|_{z=1} \\ &= N(N-1)p_A^2 \end{align} \]

With \(f''(1)\) in hand, we find the sample variance

\[ \begin{align} \sigma^2(\hat{A}) &= N(N-1)p_A^2 + Np_A - N^2p_A^2 \\ &= N^2p_A^2 - Np_A^2 + Np_A - N^2p_A^2 \\ &= Np_A(1-p_A) \end{align} \]

So that the sample standard deviation is \(\sigma(\hat{A}) = \sqrt{Np_A(1-p_A)}\).

Recall that our purpose in doing all this was to calculate the uncertainty in our estimation of the true frequency. We can illuminate our results by writing them in terms of the sample frequency.

\(\hat{p_A} = \hat{A}/N\), so, \(\langle\hat{p_A}\rangle=\hat{A}/N=p_A\), and we expect on average for the sample frequency to equal the true frequency. In other words, if we take infinitely many subsets of the population and average all our sample frequencies, we will find the true frequency.

We can re-write the sample standard deviation \[\sigma(\hat{A}) = \sqrt{\langle \hat{A}^2 \rangle - \langle \hat{A} \rangle^2} = N\sqrt{\langle p_A^2\rangle - \langle p_A \rangle ^2} = N\sigma(\hat{p_A})\] so the spread in our estimate of the true frequency (and the spread used by polling companies) is given by \(\displaystyle\sqrt{\frac{p_A(1-p_A)}{N}}\), which is plotted below for several value of \(N\).

The uncertainty has several interesting features.

As we increase our sample size, we have, initially, big gains in our absolute certainty. However, as \(N\) continues to increase, we get less and less additional certainty per unit investment in sample size. I.e. we have diminishing returns. Our results suggest that above \(N\approx 1000\), it doesn't make much sense to continue building the sample size because the error is already about \(1\%\) for all possible values of \(p\). To cut that number in half, we'd have to increase our sample size by a factor of four to 4000 respondents.

\(p(1-p)\) has a maximum at \(\frac12\), therefore populations with true frequencies close to 0.5 produce the highest uncertainty in estimations, and extreme results produce the least. Likewise, if we consider \(N\) as a cost, it is more expensive to establish a given level of certainty when \(p\) is close to \(\frac12\) than when it is close to zero or one. Here, we see the so-called **TANSTAAFL** principle at work (**T**here **A**in't **N**o **S**uch **T**hing **A**s **A** **F**ree **L**unch).

When \(p\) is far away from a coin flip, there is information-a-plenty and it is inexpensive to mine (we can achieve low uncertainty with smaller \(N\)). Close to \(\frac12\) however, there is hardly any information in the system and so, it is much more expensive to unearth any of it.

Things get a bit more involved when questions have more than two choices, and we could treat confidence intervals in more detail, but that is poll uncertainty in a nutshell.

Before we wrap up, let's take a quick look at applications of the ideas we've explored in the realm of statistical physics.

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