The Mean May Not Mean What You Think It Means: The Use and Misuse of Measures of Central Tendency

Abstract

Analysis of business studies often involves the quantification of qualitative data to derive meaningful insights and making informed decisions. One such challenge is the inappropriate use of the arithmetic mean in economic and financial modeling. The arithmetic mean is a widely used statistical measure of central tendency that sums up a set of values and divides it by the total number of observations. While the arithmetic mean is simple and intuitive, its appropriateness in financial and economic modeling highly depends upon the nature of the data and the specific research question being addressed. This creates a dilemma. Despite the business community traditionally emphasizing quantitative research modeling, the growth of artificial intelligence and big data make qualitative research more desirable, particularly in areas such as ESG scorecards and financial literacy surveys. This paper discusses the challenges presented with analyzing studies after quantifying qualitative data and provides examples of how ordinal regression and other techniques could be used to analyze qualitative variables. This is especially applicable in undergraduate education.