An increase in sample size is linked with an increase in precision of the confidence interval; the larger the sample size the more precise the interval becomes. As the confidence level goes down (e.g., from 99% to 95%) the confidence interval becomes more precise, or narrower in width. For a large sample size like the 2012 Health Information National Trends Survey (HINTS) (n= 1,500), it is acceptable to use a 95% confidence level. For smaller sample sizes, as in the case with a mean score on a class exam, it makes sense to use a 99% or 99.9% confidence level.
The image below is an example of a distribution. You should interpret from the image that the researcher can be 95% confident that the specified interval contains the true population mean.
Discuss and give examples of the types of situations in which an analyst would want to use a 95% confidence interval for estimation. Do the same for 99% and 99.9% confidence intervals. Locate and describe at least one internet resource that explains and illustrates the concepts of confidence levels and/or confidence intervals. What do you think it adds to the description in your textbook?