Descriptive and inferential statistics in organizational management

Statistics in Management: Descriptive vs. Inferential Statistics

The use of descriptive vs. inferential statistics in organizations provides decision makers, managers and leaders with the necessary insights to compete more effectively in an increasingly challenging global economic climate. The intent of this essay is to define which conditions are optimal for the use of each. Descriptive statistics by definition are more adept at the consolidating of data and its summarization (Spatz, 2008). Inferential statistics however are meant to be representative of a broader population and are developed to be statistically sound (van den Besselaar, 2003). The use of each of these types of statistics varies significantly within organizations, and has completely different interpretations when used. This essay examines how each are used to their optimal value.

Best Practices in Descriptive Statistics

There are several functional areas within organizations that rely heavily on descriptive statistics. These include accounting, business planning and analysis, financial planning, marketing, sales, product management, quality and production. Each of these functional areas are often evaluated on scorecards and benchmarks-based entirely on descriptive statistics of their activity over time (Ainslie, Leyland, 1992). Best practices in descriptive statistics for example in marketing centers on the need to accurately and succinctly summarize customer feedback about existing marketing strategies, experiences with customer service centers, and the prices paid for products as well. Descriptive statistics is an indispensible tool for evaluating which strategies are best used for retaining and growing customer loyalty as well (Ainslie, Leyland, 1992). In the area of production, descriptive statistics are very useful for evaluating the effectiveness of production techniques, systems and routing of specific products over the shop floor. This is exceptionally valuable for getting greater performance and production from less space, as lean manufacturing techniques rely on descriptive statistics for insights into how to continually improve. As these examples within organizations indicate, descriptive statistics are best for creating a synopsis or summary of a given set of variables that have a major impact on the organization. From customers to suppliers and production processes, descriptive statistics are invaluable for gaining insights into how to improve an organization over time.

Conclusion

When data needs to be accurately and succinctly summarized for analysis and decision making, descriptive statistics are most often used. Conversely when data has been captured using statistically-sound methodologies with randomization as part of the sampling plan, extrapolations of results across broader populations can be done (Spatz, 2008). The orthogonality of data in inferential statistics also is extremely useful for determining the magnitude of differences between groups, market segments and product attributes as perceived by customers (Ainslie, Leyland, 1992). Descriptive statistics are useful for the summarization of data that needs to be consolidated down to more understandable segments. Both of these approaches to statistics deliver the insights necessary in organizations to stay competitive. The use of descriptive statistics in managing the many functions of an organization, and the use of inferential statistics to interpolate survey results are both critical.