Tag Archives: Chi-Square Distributions

Rock, Paper, … Chi-Square!

In my last post I talked about how my students are benefiting from my pals in the MTBoS. Well, here I am to testify again. We are just about to wrap up our study of the Chi-Square distribution in our AP Statistics class. I used to start this unit with a little document I created based on an article in Malcolm Gladwell’s Outliers. In his book he posits the idea that there is a disparity in birth date distribution for players on a junior national hockey team. In the document I linked to I put the roster information into an EXCEL spreadsheet and I just displayed the data and asked my students to notice things. I felt like I needed to stop using this because in each of the past two years I had a number of students who read the book in an English elective and they gave away the surprise before enough conversation happened. So, I put out a twitter call for help and Bob Lochel (@bobloch) chimed in and directed me to a super helpful post over on his blog. I had seen the cool applet for playing Rock, Paper, Scissors over at the New York Times. So, I borrowed heavily from Bob (and made sure to credit him during our class discussions) and off we went to the computer lab. I prepared a handout to help organize my students and I set them loose. I asked (as you can see on the handout doc) my students to play 24 time in four different contexts. Play with random moves generated by a random integer generator or play with your gut instincts and try each against the two modes of the machine on the Times’ website. The NYT claims that the ‘robot’ plays either as a novice with no pre-programmed knowledge of how the game is played or as an expert with data gathered from other players. The novice learns your patterns as it plays you while the expert calls on a large data set of how people behave. 24 repetitions is probably not enough for the novice computer but I had some time constraints that I was trying to work around. After both of my classes played, I created a document with the data on all of the results. The next day I displayed the data and we had a pretty great conversation about the results. An important note – some of my AP Stats kiddos cannot count because the data did not come in in multiples of 24. Sigh

So I tried to start the conversation with  a simple question – Should you do better when you think about the game or should you do better by random number generation? This lead to a quick decision that the expected value of a random number generator would be an equal distribution of 8 wins, 8 ties, and 8 losses for each set of 24. Now the table is set for the important principles of the Chi-Square test. Let’s talk about the difference between observed results and expected results. We also had a great conversation about how it appeared that the random number generator actually outperformed many people – especially in the expert mode. We talked about the fact that the expert mode was trying to predict behavior and how the randomness involved here might actually play in our favor.

In the week plus since this experiment I have been able to refer back a number of times to this experiment and it feels like my students have a pretty good handle on their task here. We have our unit test tomorrow so I hope that my optimism will be supported by some data.

In addition to thanking Bob Lochel I also want to thank a new twitter pal, Jennifer Micahelis (@MichaelisMath) who engaged me in a conversation about this experience and prompted me to gather my thoughts and write about it. I definitely will revisit this experiment the next time I teach this unit.