# Statistics - Estimating Population Proportions

A population proportion is the share of a population that belongs to a particular category.

Confidence intervals are used to estimate population proportions.

## Estimating Population Proportions

A statistic from a sample is used to estimate a parameter of the population.

The most likely value for a parameter is the **point estimate**.

Additionally, we can calculate a **lower bound** and an **upper bound** for the estimated parameter.

The **margin of error** is the difference between the lower and upper bounds from the point estimate.

Together, the lower and upper bounds define a **confidence interval**.

## Calculating a Confidence Interval

The following steps are used to calculate a confidence interval:

- Check the conditions
- Find the point estimate
- Decide the confidence level
- Calculate the margin of error
- Construct the confidence interval

For example:

**Population**: Nobel Prize winners**Category**: Born in the United States of America

We can take a sample and see how many of them were born in the US.

The sample data is used to make an estimation of the share of **all** the Nobel Prize winners born in the US.

By randomly selecting 30 Nobel Prize winners we could find that:

```
```6 out of 30 Nobel Prize winners in the sample were born in the US

From this data we can calculate a confidence interval with the steps below.

## 1. Checking the Conditions

The conditions for calculating a confidence interval for a proportion are:

- The sample is randomly selected
- There is only two options:
- Being in the category
- Not being in the category
- The sample needs at least:
- 5 members in the category
- 5 members not in the category

In our example, we randomly selected 6 people that were born in the US.

The rest were not born in the US, so there are 24 in the other category.

The conditions are fulfilled in this case.

**Note:** It is possible to calculated a confidence interval without having 5 of each category. But special adjustments need to be made.

## 2. Finding the Point Estimate

The point estimate is the sample proportion (\(\hat{p}\)).

The formula for calculating the sample proportion is the number of occurences (\(x\)) divided by the sample size (\(n\)):

\(\displaystyle \hat{p} =\frac{x}{n}\)

In our example, 6 out of 30 were born in the US: \(x\) is 6, and \(n\) is 30.

So the point estimate for the proportion is:

\(\displaystyle \hat{p} = \frac{x}{n} = \frac{6}{30} = \underline{0.2} = 20\%\)

So 20% of the sample were born in the US.

## 3. Deciding the Confidence Level

The confidence level is expressed with a percentage or a decimal number.

For example, if the confidence level is 95% or 0.95:

The remaining probability (\(\alpha\)) is then: 5%, or 1 - 0.95 = 0.05.

Commonly used confidence levels are:

- 90% with \(\alpha\) = 0.1
- 95% with \(\alpha\) = 0.05
- 99% with \(\alpha\) = 0.01

**Note:** A 95% confidence level means that if we take 100 different samples and make confidence intervals for each:

The true parameter will be inside the confidence interval 95 out of those 100 times.

We use the standard normal distribution to find the **margin of error** for the confidence interval.

The remaining probabilities (\(\alpha\)) are divided in two so that half is in each tail area of the distribution.

The values on the z-value axis that separate the tails area from the middle are called **critical z-values**.

Below are graphs of the standard normal distribution showing the tail areas (\(\alpha\)) for different confidence levels.

## 4. Calculating the Margin of Error

The margin of error is the difference between the point estimate and the lower and upper bounds.

The margin of error (\(E\)) for a proportion is calculated with a critical z-value and the **standard error**:

\(\displaystyle E = Z_{\alpha/2} \cdot \sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \)

The critical z-value \(Z_{\alpha/2} \) is calculated from the standard normal distribution and the confidence level.

The standard error \(\sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \) is calculated from the point estimate (\(\hat{p}\)) and sample size (\(n\)).

In our example with 6 US-born Nobel Prize winners out of a sample of 30 the standard error is:

\(\displaystyle \sqrt{\frac{\hat{p}(1-\hat{p})}{n}} = \sqrt{\frac{0.2(1-0.2)}{30}} = \sqrt{\frac{0.2 \cdot 0.8}{30}} = \sqrt{\frac{0.16}{30}} = \sqrt{0.00533..} \approx \underline{0.073}\)

If we choose 95% as the confidence level, the \(\alpha\) is 0.05.

So we need to find the critical z-value \(Z_{0.05/2} = Z_{0.025}\)

The critical z-value can be found using a Z-table or with a programming language function:

### Example

With Python use the Scipy Stats library `norm.ppf()`

function find the Z-value for an \(\alpha\)/2 = 0.025

```
import scipy.stats as stats
```

print(stats.norm.ppf(1-0.025))

Try it Yourself »
### Example

With R use the built-in `qnorm()`

function to find the Z-value for an \(\alpha\)/2 = 0.025

```
qnorm(1-0.025)
```

Try it Yourself »
Using either method we can find that the critical Z-value is \(\approx \underline{1.96} \)

So the margin of error (\(E\)) is:

\(\displaystyle E = Z_{\alpha/2} \cdot \sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \approx 1.96 \cdot 0.073 = \underline{0.143}\)

## 5. Constructing the Confidence Interval

The lower and upper bounds of the confidence interval are found by subtracting and adding the margin of error (\(E\)) from the point estimate (\(\hat{p}\)).

In our example the point estimate was 0.2 and the margin of error was 0.143, then:

The lower bound is:

\(\hat{p} - E = 0.2 - 0.143 = \underline{0.057} \)

The upper bound is:

\(\hat{p} + E = 0.2 + 0.143 = \underline{0.343} \)

The confidence interval is:

\([0.057, 0.343]\) or \([5.7 \%, 34,4 \%]\)

And we can summarize the confidence interval by stating:

```
```The **95%** confidence interval for the proportion of Nobel Prize winners born in the US is between **5.7% and 34.4%**

## Calculating a Confidence Interval with Programming

A confidence interval can be calculated with many programming languages.

Using software and programming to calculate statistics is more common for bigger sets of data, as calculating manually becomes difficult.

### Example

With Python use the scipy and math librarires to calculate the confidence interval for an estimated proportion.

Here, the sample size is 30 and the occurences is 6.

```
import scipy.stats as stats
```

import math

# Specify sample occurences (x), sample size (n) and confidence level

x = 6

n = 30

confidence_level = 0.95

# Calculate the point estimate, alpha, the critical z-value, the standrad error, and the margin of error

point_estimate = x/n

alpha = (1-confidence_level)

critical_z = stats.norm.ppf(1-alpha/2)

standard_error = math.sqrt((point_estimate*(1-point_estimate)/n))

margin_of_error = critical_z * standard_error

# Calculate the lower and upper bound of the confidence interval

lower_bound = point_estimate - margin_of_error

upper_bound = point_estimate + margin_of_error

# Print the results

print("Point Estimate: {:.3f}".format(point_estimate))

print("Critical Z-value: {:.3f}".format(critical_z))

print("Margin of Error: {:.3f}".format(margin_of_error))

print("Confidence Interval: [{:.3f},{:.3f}]".format(lower_bound,upper_bound))

print("The {:.1%} confidence interval for the population proportion is:".format(confidence_level))

print("between {:.3f} and {:.3f}".format(lower_bound,upper_bound))

Try it Yourself »
### Example

R can use built-in math and statistics functions to calculate the confidence interval for an estimated proportion.

Here, the sample size is 30 and the occurences is 6.

```
# Specify sample occurences (x), sample size (n) and confidence level
```

x = 6

n = 30

confidence_level = 0.95

# Calculate the point estimate, alpha, the critical z-value, the standard error, and the margin of error

point_estimate = x/n

alpha = (1-confidence_level)

critical_z = qnorm(1-alpha/2)

standard_error = sqrt(point_estimate*(1-point_estimate)/n)

margin_of_error = critical_z * standard_error

# Calculate the lower and upper bound of the confidence interval

lower_bound = point_estimate - margin_of_error

upper_bound = point_estimate + margin_of_error

# Print the results

sprintf("Point Estimate: %0.3f", point_estimate)

sprintf("Critical Z-value: %0.3f", critical_z)

sprintf("Margin of Error: %0.3f", margin_of_error)

sprintf("Confidence Interval: [%0.3f,%0.3f]", lower_bound, upper_bound)

sprintf("The %0.1f%% confidence interval for the population proportion is:", confidence_level*100)

sprintf("between %0.4f and %0.4f", lower_bound, upper_bound)

Try it Yourself »