Symmetric Confidence Intervals
Investigating Symmetric Confidence Intervals for the mean of a sample from a large non-Normal population, where the population variance is known.
Author and programmer: Ron Barrow
UK Year 13, KS5, GCE A Level Mathematics - StatisticsTweet
Instructions below See also: The Central Limit Theorem
How to Use this Applet
Before trying to understand confidence intervals, make sure you understand the Central Limit Theorem. If you do not, then visit the Central Limit Theorem applet on this site.
From the Central Limit Theorem we know that for a large population (in this case 1500), the distribution of the means of random samples of the same size (in the starting case 50) is:
1 approximately Normal
2 mean = population mean
3 variance = population variance ÷ sample size This distribution is shown in yellow on the screen.
When you take a single random sample from the population and calculate its mean, how close to the population mean is it? How confident are you that they are close to one another? Does it matter how large the sample is?
When you click on "Take a Sample" above a new sample is taken. Sometimes the
mean is close to the population mean, sometimes not. But we can construct
confidence intervals from probabilities based on the fact that
the distribution of the sample mean is Normal.
Look at the 50% confidence interval (CI). About half the time the mean of the population will be in that interval, and about half the time not. The columns on the right count a 'hit rate', and after a few samples you should see that the hit rate is running around 50%. Try it and see.
Do the same for the 68%, 90%, 95% and 99% CIs. Vary the sample sample size and see what happens. If you click on "Reset Hits" it sets the hit counts to 0 and takes the first of a new batch of samples. Try it out.