When we have a large set of data, it often happens that most of the measurements will be clustered close to the mean value with the density of the observations lessening as we move away from the mean in either direction. Results of this kind displayed in a histogram show a central peak with columns of decreasing height on each side of the mean.
A data set that is symmetrical and bell-shaped about the mean, and which meets certain more precise shape requirements, is said to have an approximately normal distribution.
The shape of the normal distribution will depend on the population parameters: mean \mu and standard deviation \sigma.
In a density curve, the area beneath the curve represents probability with the area under the entire curve equal to 100\%, or 1. When data is approxiately normally distributed, the probability between 1, 2, and 3 standard deviations can be accurately summarized using the empirical rule.
The grades on a recent exam are approximately normally distributed with a mean score of 72 and a standard deviation of 4.
Construct a normal curve and label the boundaries for the empirical rule.
Find the percentage of students who scored between 64 and 68 on the exam.
If 32 students took the exam, determine the number of students expected to score 80 or more on the exam.