Parameters are descriptive measures of an entire population. However, their values are usually unknown because it is infeasible to measure an entire population. Because of this, you can take a random sample from the population to obtain parameter estimates. One goal of statistical analyses is to obtain estimates of the population parameters along with the amount of error associated with these estimates. These estimates are also known as sample statistics. A fitted distribution line is a curve based on the parameter estimates instead of on the true parameter values.

There are several types of parameter estimates:

- Point estimates are the single, most likely value of a parameter. For example, the point estimate of population mean (the parameter) is the sample mean (the parameter estimate).
- Confidence intervals are a range of values likely to contain the population parameter.

For an example of parameter estimates, suppose you work for a spark plug manufacturer that is studying a problem in their spark plug gap. It would be too costly to measure every single spark plug that is made. Instead, you randomly sample 100 spark plugs and measure the gap in millimeters. The mean of the sample is 9.2. This is the point estimate for the population mean (μ), and it informs you that the most likely value for the average gap for *all* spark plugs is 9.2 You also create a 95% confidence interval for μ which is (8.8, 9.6). This means that you can be 95% confident that the true value of the average gap for all the spark plugs is between 8.8 and 9.6.