Confidence Intervals

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?

2 thoughts on “Confidence Intervals

  • November 10, 2015 at 2:32 pm

    As I’ve said about other blogs, it doesn’t make sense to me to work against yourself and make estimation less precise in two ways – by having a high confidence level (e.g., 99%) and a small sample. Both of these make for wider intervals. In some situations, you would want to make pretty darn sure that your interval contained the parameter (a higher confidence level – e.g., if there are real world implications of being wrong) while in others, you’d be okay with getting a narrower interval, but lower confidence the parameter was actually in that interval. Most analyses use the 95% confidence interval, since it seems to balance these concerns pretty well – e.g., medium width of interval and medium confidence.

  • November 7, 2015 at 11:28 pm

    This is a good read. I agree that as the confidence level decreases, the confidence interval becomes more precise and narrow in width. It totally depends on what researcher is interested in his research, if he is looking for less error and more confidence he can go for 99% or 99.9% confidence levels but if he is interested to find the precise mean then it will be appropriate to use 95% confidence level which will also have narrower width.

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