Too Big To Fail: Large Samples and the P-Value Problem

Lin, M., H. C. Lucas, and G. Shmueli, "Too Big To Fail: Large Samples and the P-Value Problem", Information Systems Research, vol. 24, issue 4, pp. 906-917, 2013.

Biblio Tags:

Too Big To Fail: Large Samples and the P-Value Problem -- forthcoming in ISR

This weekend an important paper that I co-author with Hank Lucas and Mingfeng Lin has been accepted to the prestigious journal Information Systems Research. The paper, entitled "Too Big to Fail: Large Samples and the P-Value Problem" describes a critical challenge that occurs in modeling large samples. Publications in fields such as Information Systems as well as other social sciences have begun to rely on very large samples for testing theories.

Predicting Adoption of MVAS

Application Area:

Project Details

Term:

2012 (Nov)

Students:

Anand Prasad, Charanpreet Singh Arora, Dhruv Gandhi, Gagan Oberoi, Nikesh Lamba

University:

ISB

Presentation:

Predicting Adoption of MVAS PRESENTATION.pdf

Report:

Predicting Adoption of MVAS_REPORT.pdf

The Indian Telecom industry is amongst the most fiercely fought services market in India with more than 10 large-scale operators providing voice and data services at highly competitive prices. The Industry has witnessed significant reduction in profit margins in recent years, with the average revenues per user from voice telephony being amongst the lowest in the world. On the other hand, margins from value added services are largely seen as the next source of growth for mobile operators.