Authors: Nicolle Clements
Addresses: Department of Decision and System Sciences, Saint Joseph's University, 5600 City Avenue, Philadelphia, PA, USA
Abstract: Multiplicity of data and compounding errors is often overlooked in data analysis for applied business scenarios. Statistical theory around multiple testing provides a framework for describing appropriate error rates and offers methods to control them in order to protect against wrong conclusions. However, these multiple testing procedures are often misunderstood and underutilised in applied business problems. In this article, existing multiple testing methodologies are reviewed and summarised. Specific numeric examples are shown to illustrate the techniques and demonstrate the statistical power of each. Finally, three cases are given of business-related situations when multiple testing can be overlooked in data analysis.
Keywords: multiple testing; familywise error rate; FWER; false discovery rate; FDR; type I error; business analytics.
International Journal of Business and Data Analytics, 2019 Vol.1 No.1, pp.16 - 29
Received: 05 Apr 2018
Accepted: 02 Jul 2018
Published online: 27 Mar 2019 *