In the Chi-Square goodness of fit test, the term "goodness of fit" is used to compare the observed sample distribution with the expected probability distribution. The Chi-Square goodness of fit test determines how well a theoretical distribution (such as normal, binomial, or Poisson) fits the empirical distribution. In the Chi-Square goodness of fit test, the sample data is divided into intervals. Then, the numbers of points that fall into each interval are compared with the expected numbers of points in each interval. An example would be collecting a random sample of ten bags of candy, where each bag has 100 pieces and five flavors. The hypothesis would be that the proportions of the five flavors in each bag are the same. By doing the calculations, we expect to have equal numbers of 20 pieces of each candy in each bag (100/5 = 20). For 10 bags, we expect to have 200 pieces of candy for each flavor, which exceeds the requirement of having 5 expected values for each category.
I wrote this and was asked this question: In the scenario you described, will you be using a right-tailed test? If yes or no, explain why.