Using Confidence Intervals in Supply Chain and Operations Research
Bonett, Douglas G., Wright, Thomas A., Journal of Supply Chain Management
Confidence intervals ... should be used for major findings in both the main text of a paper and its abstract. (Gardner and Altman, Statistics in Medicine, 1986, Vol. 292, p. 746)
The usefulness of hypothesis testing methods has long been the subject of debate in the scientific community (Boring 1919; Kaiser 1960; Morrison and Henkel 1970; Hunter 1997; Wilkinson and the Task Force on Statistical Inferences. 1999), with both the tenor and tone of these criticisms becoming more pronounced in recent years (Harlow, Mulaik and Steiger 1997; Kline 2004). This controversy has resulted in much debate in the social sciences regarding the appropriate use of hypothesis testing. In an attempt to provide further clarity to this highly charged subject, we make the important distinction between informative and noninformative hypothesis testing and explain why, whenever possible, confidence intervals should replace the use of hypothesis testing.
Interestingly, business researchers have been mostly apathetic with regard to the hypothesis testing controversy. As a consequence, article-acceptance decisions in leading business journals, including those in well-regarded supply chain and operations management outlets, continue to rely almost exclusively on the results of hypothesis testing methods. In fact, and not withstanding the sage advice from Gardner and Altman (1986), many supply chain management researchers remain unfamiliar with the use and interpretation of confidence intervals. For instance, our of 234 empirical articles published in 2005, 2006 and 2007 in the Journal of Operations Management, Production and Operations Management and the Journal of Supply Chain Management, only 26 articles (11. 1 percent) reported a confidence interval. This founding is typical across the various business disciplines. Bonett and Wright (2007) reported general management journals, the Academy of Management Journal and Administrative Science Quarterly, in which only one out of the more than 130 empirical articles published in 2003 and 2004 reported a confidence interval.
This continued reliance on hypothesis testing is the result of a number of misconceptions regarding populations, population parameters and the type of information provided by tests of hypotheses (Bonett and Wright 2007). In this article we will: (1) briefly review the basic concepts of a population and a population parameter which provide the necessary foundation for the interpretation of confidence intervals; (2) outline limitations of statistical hypothesis testing: (3) provide five examples to illustrate how confidence intervals may be used in place of hypothesis testing methods and (4) close with five publication guideline suggestions designed to foster a more effective use of confidence intervals in supply chain research. We begin our discussion with some basic definitions and ideas regarding populations and population parameters.
POPULATIONS AND POPULATION PARAMETERS
Research involving the planning, implementing and controlling of the operations of such business functions as the supply chain often requires the use of inferential statistical methods to answer the questions of interest. Answers to these research questions typically involve an understanding of the characteristics of some large se of units. The units might be firms, suppliers, customers, employees, products, packages, orders or items in inventory, just to name a few. The set of units under investigation is called the study population. Some examples of study populations in supply chain research are: all Fortune 1,000 firms, all customers in the firm's database, all employees who work in a network of firms, all packages shipped by a firm during some period of time, all orders received by the firm during some period of time and all items of a particular type in a firm's inventory.
The researcher will typically be interested in particular characteristics of the unites that make up the study population. …