Last week, we talked about confidence intervals for means coming from numerical data. Today, we'll do something quite similarfor proportions that arise from categorical data.

Like, last Wednesday's presentation, this is related to sections 5.1 and 5.2 of our text but follows that even more closely.

Suppose we take a random sample of 100 North Carolinians and check whether they are left handed or right handed. If 13 of them are left handed, we would say that the proportion of them who are left handed is $13\%$. That $13\%$ is a *sample proportion* $\hat{p}$ that estimates the *population proportion* $p$.

Note that a proportion is a numerical quantity, even though the data is categorical. Thus, we can compute confidence intervals in a very similar way. Just as with sample means, the sampling process leads to a random variable and, if certain assumptions are met, then we can expect that random variable to be normally distributed.

One notable computational difference between finding confidence intervals for propritions as compared to those for means is how we find the underlying standard deviation. For numerical data, we simply estimate the population standard deviation with standard deviation for the sample. For a sample proportion, if we identify success (being left handed, for example) with a $1$ and failure as a $0$, then (as we know from our discussion of the binomial distribution) the resulting standard deviation is

$$\sigma = \sqrt{p(1-p)}.$$It follows that the standard error is

$$SE = \frac{\sigma}{\sqrt{n}} = \sqrt{\frac{p(1-p)}{n}}.$$Suppose we draw a random sample of 132 people and find that 16 of them have blue eyes. Use this data to write down a $95\%$ confidence interval for the proportion of people with blue eyes.

*Solution*: We have $\hat{p}=16/132 \approx 0.1212$ and

Thus, our confidence interval is

$$0.1212 \pm 2\times0.0284 = [0.0644, 0.178].$$If you read the details of political surveys, you're likely to come across the term
"margin of error" at some point. Five Thirty Eight,
for example, maintains a running
Trump approval rating page.
The page also points to poll details for a *slew* of polls. Check out the first one, namely
the Gallup poll.
There, we read "Daily results are based on telephone interviews with approximately 1,400
national adults; Margin of error is $\pm 3$ percentage points". What does that mean?

When we write a confidence interval as
$$s \pm z^* \times SE,$$
Then, $z^* \times SE$ is the *margin of error*. Geometrically, it's the distance that the
interval extends in either direction from the measured statistic $s$.

So, where's the $\pm 3$ come from?

Suppose we're writing down a confidence interval for a proportion. In this case, approve or disapprove. If the actual proportion is $p$ and our sample size is $n$, then the standard error is

$$\sqrt{\frac{p(1-p)}{n}}.$$In our case, the take $n\approx 1500$. Furthermore the *biggest* that $p(1-p)$ can be is
$1/4$. You can see this by taking a look at a graph:

In our example, the sample size was 1500. Thus, the standard error is at *most*

Now, for a $95\%$ confidence interval, we take $z^* = 2$ so that our margin of error is at most

$$ME \leq 2\times0.01290994 \approx 0.026,$$which is rounded up to 3 percentage points.

This is a common thing to shoot for in political polls, which is why you often see sample sizes close to 1500.

In early November of last year, FiveThirtyEight reported that Donald Trump was only 3.3 percentage points behind Hilary Clinton - almost within the margin of error. In fact, Clinton ended up winning the popular vote by $2.1\%$.

A recent poll by the Brookings Institute asks the following question of 1578 college students: "Is hate speech constitutionally protected?" Here are the results expressed as percentages:

Political Affiliation | Type of College | Gender | ||||||
---|---|---|---|---|---|---|---|---|

All | Dem | Rep | Ind | Public | Private | Female | Male | |

Yes | 39 | 39 | 44 | 40 | 38 | 43 | 31 | 51 |

No | 44 | 41 | 39 | 44 | 44 | 44 | 49 | 38 |

Don’t know | 16 | 15 | 17 | 17 | 17 | 13 | 21 | 11 |

Use this to write down a 95% confidence interval for the percentage of students who believe
that hate speech is *not* constitutionally protected.

We have $\hat{p}=0.44$ yielding an estimate of the standard error of

$$SE = \sqrt{\frac{\hat{p}(1-\hat{p})}{n}} = \sqrt{\frac{0.44\times0.56}{1578}}\approx0.0125.$$Thus, our confidence interval is

$$0.44 \pm 2\times0.0125 = [0.415,0.456].$$Recall that the margin of error generally depends on three things:

- Confidence level,
- underlying standard deviation, and
- the
*sample size*.

Sometimes, we require a specific confidence level and margin of error and, for a sample proportion, the underling standard deviation $\sqrt{p(1-p)}$ is never larger than $1/2$. Thus, we can always obtain the desired confidence level and margin of error by choosing sample size large enough.

In order to choose the sample size, we simply set up the inequality

$$z^*\sqrt{\frac{p(1-p)}{n}} < ME,$$where $z^*$ corresponds to the desired confidence level and $ME$ is the desired confidence level. Since $\sqrt{p(1-p)}<1/2$, this simplfies to

$$z^*\frac{1/2}{\sqrt{n}} < ME \: \text{ or } \: n>\frac{{z^*}^2}{4ME^2}.$$Suppose we wish to determine the percentage of voters who support our candidate. We'd like a $95\%$ level of confidence to $\pm2\%$ points. What sample size should guarantee this?

**Simple solution**: For a 95% level of confidence, we might take $z^*=2$ together with the given margin of error $ME=0.02$ to get

Thus, a pollster would probably be happy with $n=2500$ folks in the poll.

We can get a more precise (possibly smaller) sample size by using more precise estimates to $z^*$. In fact, for the homework, you need three digits of precision for your $z^*$ multiplier. For 90, 95, and 99%, these are

Conf: | 90% | 95% | 99% |
---|---|---|---|

$z^*$ | 1.282 | 1.960 | 2.326 |

In the previous problem, we would have:

$$n>\frac{{z^*}^2}{4ME^2} = \frac{1.960^2}{2\times0.02^2} = 2400.$$Thus, the HW would like to see 2401.