Confidence Intervals

Confidence intervals are a fundamental concept in statistics, providing a range of values within which a population parameter is expected to lie. They are widely used in data science, research, and decision-making to quantify the uncertainty associated with estimates. This article explores how to construct and interpret confidence intervals, understand confidence levels, and calculate the margin of error with practical examples.

What is a Confidence Interval?

A confidence interval is a range of values, derived from a sample, that is likely to contain the true value of a population parameter with a specified level of confidence. Instead of providing a single estimate (point estimate), a confidence interval gives a range that is believed to cover the true parameter.

Example: Estimating a Population Mean

Suppose we want to estimate the average height of all students in a university. Instead of just calculating the mean height from a sample and stating that as the population mean, we construct a confidence interval that provides a range within which the true mean height is likely to fall.

Confidence Level

The confidence level is the probability that the confidence interval contains the true population parameter. It is typically expressed as a percentage, such as 95% or 99%.

Common Confidence Levels

• 90% Confidence Level: Indicates that 90% of the confidence intervals constructed in this manner would contain the true parameter.
• 95% Confidence Level: Indicates that 95% of the confidence intervals would contain the true parameter.
• 99% Confidence Level: Indicates that 99% of the confidence intervals would contain the true parameter.

Relationship Between Confidence Level and Interval Width

• Higher Confidence Level: Leads to a wider interval because you need a broader range to be more certain that the interval contains the true parameter.
• Lower Confidence Level: Leads to a narrower interval, but with less certainty that it contains the true parameter.

Margin of Error

The margin of error quantifies the uncertainty in the estimate. It is half the width of the confidence interval and depends on the sample size, variability in the data, and the confidence level.

Formula for Margin of Error

For a confidence interval around a mean, the margin of error (ME) is calculated as:

$$\text{Margin of Error (ME)} = z^* \times \frac{\sigma}{\sqrt{n}}$$

Where:

• $z^*$ is the critical value from the standard normal distribution corresponding to the desired confidence level.
• $\sigma$ is the standard deviation of the population (or an estimate if the population standard deviation is unknown).
• $n$ is the sample size.

Example: Margin of Error Calculation

Suppose we want to estimate the average IQ score of students at a university with a 95% confidence level. We have a sample of 100 students, a sample mean of 110, and a sample standard deviation of 15.

• For a 95% confidence level, $z^* \approx 1.96$ (from the standard normal distribution table).
• The margin of error is:

$$\text{ME} = 1.96 \times \frac{15}{\sqrt{100}} = 1.96 \times 1.5 = 2.94$$

Interpreting the Result

The margin of error is 2.94, so the 95% confidence interval for the mean IQ score is:

$$\text{Confidence Interval} = 110 \pm 2.94 = [107.06, 112.94]$$

This means we are 95% confident that the true mean IQ score lies between 107.06 and 112.94.

Constructing Confidence Intervals

Confidence intervals can be constructed for various population parameters, including means, proportions, and differences between means or proportions.

1. Confidence Interval for a Population Mean

When Population Standard Deviation ($\sigma$) is Known

If the population standard deviation is known, the confidence interval for the population mean ($\mu$) is given by:

$$\text{CI} = \bar{x} \pm z^* \times \frac{\sigma}{\sqrt{n}}$$

Where:

• $\bar{x}$ is the sample mean.
• $z^*$ is the critical value from the standard normal distribution.
• $\sigma$ is the population standard deviation.
• $n$ is the sample size.

When Population Standard Deviation ($\sigma$) is Unknown

When the population standard deviation is unknown, the sample standard deviation ($s$) is used, and the $t$-distribution is applied instead of the $z$-distribution:

$$\text{CI} = \bar{x} \pm t^* \times \frac{s}{\sqrt{n}}$$

Where:

• $t^*$ is the critical value from the $t$-distribution with $n-1$ degrees of freedom.

Example: Constructing a Confidence Interval

Suppose we have a sample of 50 students with a mean test score of 75 and a sample standard deviation of 10. We want to construct a 95% confidence interval for the population mean test score.

• $t^*$ for 49 degrees of freedom (from the $t$-distribution table) at a 95% confidence level is approximately 2.009.
• The confidence interval is:

$$\text{CI} = 75 \pm 2.009 \times \frac{10}{\sqrt{50}} = 75 \pm 2.84 = [72.16, 77.84]$$

We are 95% confident that the true mean test score lies between 72.16 and 77.84.

2. Confidence Interval for a Population Proportion

When estimating a population proportion ($p$), the confidence interval is given by:

$$\text{CI} = \hat{p} \pm z^* \times \sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$$

Where:

• $\hat{p}$ is the sample proportion.
• $z^*$ is the critical value from the standard normal distribution.
• $n$ is the sample size.

Example: Constructing a Confidence Interval for a Proportion

Suppose a survey of 400 voters shows that 60% support a particular candidate. We want to construct a 95% confidence interval for the true proportion of voters who support the candidate.

• $\hat{p} = 0.60$, $z^* \approx 1.96$ for a 95% confidence level.
• The confidence interval is:

$$\text{CI} = 0.60 \pm 1.96 \times \sqrt{\frac{0.60(1-0.60)}{400}} = 0.60 \pm 0.048 = [0.552, 0.648]$$

We are 95% confident that the true proportion of voters who support the candidate lies between 55.2% and 64.8%.

3. Confidence Interval for the Difference Between Two Means

When comparing two independent samples, the confidence interval for the difference between two population means ($\mu_1 - \mu_2$) is given by:

$$\text{CI} = (\bar{x}_1 - \bar{x}_2) \pm t^* \times \sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}$$

Where:

• $\bar{x}_1$ and $\bar{x}_2$ are the sample means.
• $s_1^2$ and $s_2^2$ are the sample variances.
• $n_1$ and $n_2$ are the sample sizes.
• $t^*$ is the critical value from the $t$-distribution with degrees of freedom approximated using the Welch-Satterthwaite equation.

Example: Comparing Two Means

Suppose we want to compare the mean test scores of two different teaching methods. We collect data from two independent samples of students:

• Sample 1: $n_1 = 30$, $\bar{x}_1 = 85$, $s_1 = 5$
• Sample 2: $n_2 = 35$, $\bar{x}_2 = 80$, $s_2 = 6$

We want to construct a 95% confidence interval for the difference in mean test scores.

• $t^*$ for approximately 60 degrees of freedom is 2.000 (using a $t$-distribution table).

The confidence interval for the difference is:

$$\text{CI} = (85 - 80) \pm 2.000 \times \sqrt{\frac{5^2}{30} + \frac{6^2}{35}} = 5 \pm 2.82 = [2.18, 7.82]$$

We are 95% confident that the difference in mean test scores between the two teaching methods is between 2.18 and 7.82 points.

Interpreting Confidence Intervals

Misinterpretations to Avoid

• Confidence Level: A 95% confidence level does not mean there is a 95% probability that the interval contains the true parameter. It means that if we were to take many samples and construct intervals, 95% of those intervals would contain the true parameter.
• Fixed Interval: The true parameter is either within the interval or not; the interval itself does not change. The confidence level pertains to the method, not the specific interval.

Practical Implications

Confidence intervals provide a range of plausible values for the parameter, offering more information than a point estimate alone. They are used in various fields, including healthcare, finance, and social sciences, to guide decision-making.

Conclusion

Confidence intervals are a powerful tool in statistics, providing a range of values that likely contain the true population parameter. By understanding how to construct and interpret confidence intervals, you can quantify the uncertainty associated with your estimates and make more informed decisions.