Students Learn That Statistics Studies can be 'Sweet'

Shoshana Michaeli already considers the seven students in her AP Statistics class “statisticians” and if she could give them certificates declaring them as such, she most certainly would.

The math teacher has been teaching her students how to successfully perform significant studies which would be used in real life such as determining the distribution of race within a society or in a work environment. In the classroom she uses fun samples such as candy for the students to use in order to work through their hypotheses.

The students have learned all the formulas and steps on how to successfully do these studies and Michaeli said when they go to college they will be able to use the tools they gained here to perform an observational study of which to draw a conclusion from.

Last Friday morning the students did a "sweet" study - comparing the distribution of yellow Skittles verses yellow M&Ms both which are produced by the Mars corporation.

Michaeli supplied them with large samples for which to conduct their study – bags upon bags of Skittles and M&Ms were laid out onto the desks for the students to count through. They asked Michaeli how the data was collected as varying factors need to be considered such as where and when the samples were purchased. She told them that she bought some bags at A&P and Stop and Shop and they were bought at different times of day.

The students separated the yellow Skittles and yellow M&Ms, counted the total number of candies and found that the proportion of yellow Skittles is different from the proportion of yellow M&Ms.

They have done other studies using candies such as Hershey’s miniatures where they tried to determine what is the most favored candy in the bag.

Next week they will do another study – this time involving paper airplanes. Michaeli provided the students with instructions on how to build a straight wing paper plane and a sleek jet. They will then stand in the hallway and fly their paper planes measuring the distance of each. The students will then be required to compare the mean, or the average, distance between the two different models.