It is good to think of the normal distribution as a “normal”, or regular dispersal of data. It is likely no experimental dissemination of data will match it exactly, buy countless distributions of data in the real world look like the normal curve. If a real distribution that researchers are studying comes quite close to the normal distribution, it suggests the researchers will already know a great deal about the real distribution without further work (Vogt, 2007). The most common data are toward the middle, that has a mean of zero, or a z-score of zero. The least common data are far from the mean and from the mode and the median, which are all identical in a normal distribution (Vogt, 2007).
Real World Example: Normal Distribution
An example of something we might expect to see distributed normally would be:
The average age in months, that the average child begins to walk.
(Here we would expect to see children achieve this developmental milestone at a similar time.)
Real World Example: NOT a Normal Distribution
An example of something we might not expect to see distributed normally would be:
Annual incomes of members of the working population.
(Here we would expect to see data that yielded higher standard deviations than the previous example.)
Vogt, P.W., (2007). Quantitative Research Methods for Professionals. Boston, MA: Allyn and Bacon.