We use inferential statistics to make generalizations about an unknown population. The simplest way of doing this is to use the sample data in making a point estimate of a population parameter. We realize that due to sampling variability, the point estimate is most likely not the exact value of the population parameter, though it should be close. After calculating point estimates, we can build off of them to construct interval estimates called confidence intervals.

Confidence Intervals

A confidence interval is another type of estimate, but, instead of being just one number, it is a range of reasonable values in which we expect the population parameter to fall. Since a point estimate may not be perfect due to variability, the idea is to build an interval based on a point estimate to hopefully capture the parameter of interest in the interval. There is no guarantee that a given confidence interval does capture the parameter, but there is a predictable probability of success. It is important to keep in mind that the confidence interval itself is a random variable, while the population parameter is fixed.

If you worked in the marketing department of an entertainment company, you might be interested in the mean number of songs a consumer downloads a month from iTunes. If so, you could conduct a survey and calculate the sample mean, . You would use to estimate the population mean. The sample mean, , is the point estimate for the population mean, μ.

Continuing the iTunes example, suppose we do not know the population mean, μ, but we do know that the population standard deviation is σ = 1 and that our sample size is 100. Then, by the central limit theorem, the standard deviation for the sample mean is:

= .

The empirical rule, which applies to bell-shaped distributions, says that the sample mean, , will be within two standard deviations of the population mean, μ, in approximately 95% of the samples. For our iTunes example, two standard deviations is (2)(0.1) = 0.2. The sample mean is likely to be within 0.2 units of μ.

Because is within 0.2 units of μ, which is unknown, then μ is likely to be within 0.2 units of in 95% of the samples. The population mean, μ, is contained in an interval whose lower number is calculated by taking the sample mean and subtracting two standard deviations (2)(0.1) and whose upper number is calculated by taking the sample mean and adding two standard deviations. In other words, μ is between – 0.2 and + 0.2 in 95% of all the samples.

For the iTunes example, suppose that a sample produced a sample mean = 2. Therefore, the unknown population mean μ is between – 0.2 = 2 – 0.2 = 1.8 and + 0.2 = 2 + 0.2 = 2.2.

We can say that we are about 95% confident that the unknown population mean number of songs downloaded from iTunes per month is between 1.8 and 2.2. The approximate 95% confidence interval is (1.8, 2.2).

This approximate 95% confidence interval implies two possibilities. Either the interval (1.8, 2.2) contains the true mean μ, or our sample produced an that is not within 0.2 units of the true mean μ. The second possibility happens for only 5% of all the samples (95–100%).

Remember that confidence intervals are created for an unknown population parameter. Confidence intervals for most parameters have the form:

(Point Estimate ± Margin of Error) = (Point Estimate – Margin of Error, Point Estimate + Margin of Error)

The margin of error (MoE) depends on the confidence level or percentage of confidence and the standard error of the mean.

When you read newspapers and journals, some reports will use the phrase “margin of error.” Other reports will not use that phrase, but include a confidence interval as the point estimate plus or minus the margin of error. These are two ways of expressing the same concept.

A confidence interval for a population mean with a known standard deviation is based on the fact that the sample means follow an approximately normal distribution. If our sample has a mean of = 10, we can construct the 90% confidence interval (5, 15) where MoE = 5.

Calculating the Confidence Interval

To construct a confidence interval for a single unknown population mean, μ, where the population standard deviation is known, we need as an estimate for μ and we need the margin of error. The sample mean, , is the point estimate of the unknown population mean, μ.

A confidence interval estimate will have the following form:

PE-MoE, PE+MoE

As a result, a confidence interval for the unknown population mean μ in symbols would look like:

– MoE, + MoE

Remember, the margin of error depends mainly on the confidence level (CL). The confidence level is often considered the probability that the calculated confidence interval estimate will contain the true population parameter. However, it is more accurate to state that the confidence level is the percent of confidence intervals that contain the true population parameter when repeated samples are taken. Most often, it is up to the person constructing the confidence interval to choose a confidence level of 90% or higher because that person wants to be reasonably certain of their conclusions.

There is another probability called alpha (α), which is related to the confidence level and represents the chance that the interval does not contain the unknown population parameter. Mathematically, this looks like:

α + CL = 1

To construct a confidence interval estimate for an unknown population mean, we need data from a random sample. The steps to construct and interpret the confidence interval are:

1. Calculate the sample mean, , from the sample data. Remember, in this section, we already know the population standard deviation, σ.
2. Find the z-score (critical value) that corresponds to the confidence level.
3. Calculate the margin of error.
4. Construct the confidence interval.
5. Write a sentence that interprets the estimate in the context of the situation in the problem. (Use the words of the problem to explain what the confidence interval means.)

We will first examine each step in more detail and then illustrate the process with some examples.

Changing the Confidence Level

A confidence interval for a population mean with a known standard deviation is based on the fact that the sample means follow an approximately normal distribution. Suppose that our sample has a mean of = 10, and we have constructed the 90% confidence interval (5, 15) where the MoE = 5.

To get a 90% confidence interval, we must include the central 90% of the probability of the normal distribution. If we include the central 90%, we leave out a total of α = 10% in both tails, or 5% in each tail, of the normal distribution.

To capture the central 90%, we must go out 1.645 standard deviations on either side of the calculated sample mean. The value 1.645 is the z-score from a standard normal probability distribution that results in an area of 0.90 in the center, an area of 0.05 in the far left tail, and an area of 0.05 in the far right tail.

It is important that the standard deviation used must be appropriate for the parameter we are estimating, so in this section, we need to use the standard deviation that applies to sample means, which is . The fraction , is commonly called the “standard error of the mean” in order to distinguish clearly the standard deviation for a mean from the population standard deviation, σ.

In summary, as a result of the central limit theorem:

• is normally distributed; that is, ~ N(μ_X, .
• When the population standard deviation, σ, is known, we use a normal distribution to calculate the margin of error.

Finding the Critical Value

When we know the population standard deviation, we use a standard normal distribution to calculate the margin of error and construct the confidence interval. We need to find the value of z that puts an area equal to the confidence level (in decimal form) in the middle of the standard normal distribution Z ~ N(0, 1). This z-score is also called a critical value.

The confidence level is the area in the middle of the standard normal distribution. Since CL = 1 – α, α is the area that is split equally between the two tails. Each of the tails contains an area equal to .

The z-score that has an area to the right of is denoted by .

NOTE: Remember to use the area to the LEFT of .

Calculating the Margin of Error

The margin of error formula for an unknown population mean and a known population standard deviation is as follows:

MoE =

Constructing the Confidence Interval

A confidence interval estimate has the format – MoE, + MoE.

The graph gives a picture of the entire situation.

CL + + = CL + α = 1.

Writing the Interpretation

The interpretation should clearly state the confidence level, explain what population parameter is being estimated (here, the population mean), and state the confidence interval (both endpoints). “We can be         % confident that the interval we created,            to           , captures the true population mean.” It should include the context of the problem and appropriate units.

Be careful that you do not associate the confidence level with the parameter itself. Your parameter is a fixed value; what is changing is the sample you take and the interval you calculate. We always want to associate the CL% with the sampling process and the interval.

Figure References

Figure 5.7: Sebastian Gomez (2020). yellow green and red candies on white ceramic round plate. Unsplash license. https://unsplash.com/photos/w9pT3v9z1CM

Figure 5.8: Kindred Grey (2024). 90% confidence level. CC BY-SA 4.0.

Figure 5.9: Kindred Grey (2024). Constructing the confidence interval. CC BY-SA 4.0.

Figure Descriptions

Figure 5.7: White bowl with lots of M&Ms sits on a white table.

Figure 5.8: Population is larger than the sample. Population mean is slightly above the sample mean. The sample mean is 10 and the margin of error is 10+5 and 10-5. 90% confidence interval: To 90%, the mean value of the population is in the range 5, 15.

Figure 5.9: Population is larger than the sample. Population mean is slightly above the sample mean. 95% confidence interval: To 95%, the mean value of the population is in this range (x bar + MOE, x bar – MOE).