2011 IEEE 27th International Conference on Data Engineering (2011)
Apr. 11, 2011 to Apr. 16, 2011
Guimei Liu , Department of Computer Science, National University of Singapore, Singapore
Mengling Feng , Data Mining Department, Institute for Infocomm Research, Singapore
Yue Wang , Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore
Limsoon Wong , Department of Computer Science, National University of Singapore, Singapore
See-Kiong Ng , Data Mining Department, Institute for Infocomm Research, Singapore
Tzia Liang Mah , Data Mining Department, Institute for Infocomm Research, Singapore
Edmund Jon Deoon Lee , Pharmacology Department, National University of Singapore, Singapore
Hypothesis testing is a well-established tool for scientific discovery. Conventional hypothesis testing is carried out in a hypothesis-driven manner. A scientist must first formulate a hypothesis based on his/her knowledge and experience, and then devise a variety of experiments to test it. Given the rapid growth of data, it has become virtually impossible for a person to manually inspect all the data to find all the interesting hypotheses for testing. In this paper, we propose and develop a data-driven system for automatic hypothesis testing and analysis. We define a hypothesis as a comparison between two or more sub-populations. We find sub-populations for comparison using frequent pattern mining techniques and then pair them up for statistical testing. We also generate additional information for further analysis of the hypotheses that are deemed significant. We conducted a set of experiments to show the efficiency of the proposed algorithms, and the usefulness of the generated hypotheses. The results show that our system can help users (1) identify significant hypotheses; (2) isolate the reasons behind significant hypotheses; and (3) find confounding factors that form Simpson's Paradoxes with discovered significant hypotheses.
Y. Wang et al., "Towards exploratory hypothesis testing and analysis," 2011 IEEE 27th International Conference on Data Engineering(ICDE), Hannover, Germany, 2011, pp. 745-756.