I found a great video at Khan Academy on how to perform a Chi-Square Goodness of Fit test. What's great about Khan Academy is that they explain the concept, show an example, and the provide a short quiz to test your understanding. I use the Chi-Square test all the time when analyzing warranty data. For example, our F-Series trucks are manufactured with both gasoline and diesel engines. Let's say that I am investigating a lighting warranty issue. If I have a suspicion that this warranty issue might be more prevalent in a gasoline-powered truck, I can tally the number of warranty claims for the lighting issue for each engine type and also tally the sales volumes for each engine type. The null hypothesis assumption is that if the gasoline-powered sales are 70% of total sales, that the gasoline-powered lighting issue warranty claims should also be about 70% of the total lighting issue warranty claims. If the Chi-Square test comes back as significant, then that will help me scope my project into looking at what is different between the gasoline and diesel trucks for the lighting system. If the Chi-Square test does not come back as significant, then I know that engine type is not a contrasting factor to investigate.