StatsVille is a game where the player assumes the role of a city health administrator. On their first day on the job, they have been notified that the city is faced with a potentially severe epidemic that they need to stop.
In the initial stages/levels of the game, players are asked to determine an appropriate treatment strategy (i.e. whether treatment A or treatment B will more effectively stop the spread of the disease). Players can simply make choices based upon the visualizations, but they will develop better strategies (i.e. improve their chances of winning) if they thoroughly understand the data and techniques for analyzing it. Players get daily feedback based upon their strategy. If they have a good strategy they will see that they are stopping the spread of the disease.
Multiple levels of the game are developed by varying model parameters. In the initial stages, the game is fairly easy to win, but at each level the game gradually advances in difficulty and requires students to build upon strategies used in previous levels.
The following link allows you to play the StatsVille Game.
You may be asked to install Unity Web Player, this may take a few minutes.
Many browsers will require you to allow popups before they will run these stat2labs games.
Data visualizations for all data from Level 1 is available at StatsVille App.
Data visualizations for all data is available at StatsVille App.
All data from the game is available at StatsVille Data.
Multiple Two-Proportion Tests: Student Handout
This activity uses the StatsVille game to demonstrate the challenges of using multple hypothesis tests to draw conclusions. Students recognize that each random sample from a population varies, and this causes the corresponding p-value to also change with each sample. The emphasis is on using data to draw conclusions, even when the standard assumptions for hypothesis tests may not hold.
Thanks to Professor Anya Vostinar and Grinnell students Mariam Nadiradze, Tianhao (Mike) Zou, Ritika Agarwal, Jimin Tan, Jemuel Santos, Hoang Cao, Kevin Connors, Houfu Yan, and Anaan Ramaay for creating, editing and maintaining the on-line game. Thanks to Yuanqi Zhao, Yuyin Sun, and Matthew Palmeri for assistance with the data visualizations.