Typically, when interpreting data, there is a trade-off between having a small confidence interval and a high confidence level. In other words, you can tighten the confidence interval, but you will be less sure the true value will fall within it. For example, companies G, H, and I from the previous Practice Run were all produced using the same data.

Increasing the sample size will allow you to reduce the size of the confidence interval while keeping a high confidence level, which is one reason why having a sufficiently large sample size for a study is important.

The sample size, margin of error, and confidence level are three pieces of information that are usually given when survey data is reported. This information explains how sure the researcher is of their claim(s). A good study will have a large sample size, a high confidence level, and a low margin of error. A claim that does not fit these criteria or a report that does not give this information should cause you to question the findings.