**Introduction**

A confidence interval (CI) is an interval estimate of a population parameter and is used to indicate the reliability of an estimate and can be interpreted as the range of values that would contain the true population value 95% of the time if the survey were repeated on multiple samples.

The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. We have therefore produced a single estimate in a way that, if repeated indefinitely, would result in 90% of the confidence intervals formed containing the true value.

Confidence intervals are one way to represent how "good" an estimate is; the larger a 90% confidence interval for a particular estimate, the more caution is required when using the estimate. Confidence intervals are an important reminder of the limitations of the estimates.

**Practical example**

Say you were interested in the mean weight of 10-year-old girls living in the United States. Since it would have been impractical to weigh all the 10-year-old girls in the United States, you took a sample of 16 and found that the mean weight was 90 pounds. This sample mean of 90 is a point estimate of the population mean. A point estimate by itself is of limited usefulness because it does not reveal the uncertainty associated with the estimate; you do not have a good sense of how far this sample mean may be from the population mean. For example, can you be confident that the population mean is within 5 pounds of 90? You simply do not know.

Confidence intervals provide more information than point estimates. Confidence intervals for means are intervals constructed using a procedure (presented in the next section) that will contain the population mean a specified proportion of the time, typically either 95% or 99% of the time. These intervals are referred to as 95% and 99% confidence intervals respectively. An example of a 95% confidence interval is shown below:

72.85 < μ < 107.15

There is good reason to believe that the population mean lies between these two bounds of 72.85 and 107.15 since 95% of the time confidence intervals contain the true mean.

If repeated samples were taken and the 95% confidence interval computed for each sample, 95% of the intervals would contain the population mean. Naturally, 5% of the intervals would not contain the population mean.

**Simulation in R**

Let's simulate confidence interval in R and plot the result.

Say you have a sample from a population. Given that sample, you want to determine a confidence interval for the population’s mean.

We'll apply the `t.test`

function to your sample x `t.test(x)`

. The output includes a confidence interval at the 95% confidence level. To see intervals at other levels, use the `conf.level`

argument.

x = sample(10, 20, replace=T) # [1] 1 3 5 3 9 6 2 9 8 6 7 4 10 8 5 4 7 2 7 5 t.test(x) # One Sample t-test # data: x # t = 9.6021, df = 19, p-value = 1.008e-08 # alternative hypothesis: true mean is not equal to 0 # 95 percent confidence interval: # 4.340241 6.759759 # sample estimates: # mean of x # 5.55 lower = t.test(x)$conf.int[1] upper = t.test(x)$conf.int[2] amount = length(x) barplot(x, main="Confidence Interval") # plot mean abline(h=mean(x), lty=1, col="red") text(amount, mean(x), "mean", col="red", adj=c(0, -0.2)) # plot upper abline(h=upper, lty=2, col="red") text(amount, upper, "upper", col="red", adj=c(0, -0.2)) # plot lower abline(h=lower, lty=2, col="red") text(amount, lower, "lower", col="red", adj=c(0, -0.2))

**Useful links**

Statisticians, like artists, have the bad habit of falling in love with their models.
*George Box*

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