The Significance of Descriptive and Inferential Statistics in the Decision Making Process

Data helps people to make more informed and effective decisions. The two types of statistical methods are inferential and descriptive statistics, which serve a significant purpose in analyzing and solving problems. It provides a better understanding of the issue at hand, helping managers to identify risks and opportunities during the decision-making process with ease. While people use inferential statistics to explain situations, descriptive statistics can help interpret available data, hence a vital factor in making rational decisions. Despite the cost of collecting data, inferential and descriptive statistics are crucial in the decision-making process because they validate arguments, explain connections between variables, and guarantee quality products and services.

It is easier to convince people to move in a particular way by supporting an argument with data. Decision-making is a complex process because one is required to persuade almost everyone to accept a proposed solution to a given problem, despite the risks involved. When it comes to assuring people that a given decision is needed, statistics become significant. Descriptive statistics condense data into a summary, enabling the decision-makers to assess the problem in a more manageable form (Kaur et al., 2018). Again, whenever data is involved, making inferences becomes pivotal. Therefore, inferential statistics help people understand the kind of descriptive data presented to support an argument, raising the chances of agreeing on a particular decision. For example, for a manager to argue that increasing production during the summer can help increase revenue, data that describes the change in quantity demanded during such a period can be influential. Descriptive and inferential statistics corroborate arguments and helps everyone understand the connections between different variables.

Statistics in decision-making reveals the relationships, patterns, and trends which can create significant opportunities for a company. For example, a careful review of data can help realize the relationship between variables that influence the growth of an organization in the market. Decisions made based on unsupported arguments are usually not the best for a company focused on sustainability. A long-term strategy depends partly on careful analysis of market opportunities, explainable by statistical analysis. For example, using bars and charts, it is possible to deduce the relationship between sales offers and revenue. The management can also use forecasts to infer the progress of previous decisions and identify opportunities to either change or improve them. A focus on continuous improvement through a careful analysis of data can improve the quality of products and services.

Statistics provide the means to measure and control production processes to reduce variations in quality. A company can create a competitive advantage by offering superior products and services than its competitors. “One neglected method of improving product quality, while increasing production efficiency, is the application of statistical methods to increase management’s understanding of control over the production process” (Sauers, 1983, p. 1). Sauers implies that data gives a clear understanding of the production process, the input and output variabilities. Decision-makers can hold divergent opinions on the same variables. Yet, descriptive and inferential statistics permit the modeling of the relationships between variables to come up with a unanimous conclusion at the preselected risk of making wrong decisions. Descriptive and inferential statistics are vital in quality control and creates significant opportunities for companies.

Cadbury Schweppes is one of the oldest family-run businesses globally. The company sells products such as ginger ale, tonic water, and carbonated lemonade. Its success emerged from the focus on value, especially the Managing for Value (MFV) approach, with which it built long-term value for its shareholders. In developing a strategy, it is decisive for the management to ensure quality data analysis (Moore, 2000). Cadbury supported the success of a value-driven approach by developing a sustainable brand, building products through innovation, and encouraging acquisitions that would guarantee sustainability. To achieve such success by making effective long-term and competitive strategies, the company must have relied on data analysis and crucial inferences. Despite the company’s success, it is discernible that inferential and descriptive statistics could offer the company more opportunities through better decisions.

 Cadbury Schweppes can improve its decision-making process by encouraging more use of descriptive and inferential statistics. Data from in-house research and external consultants can help the company representatives craft a decision-making model that supports its value-based approach. In-house research can offer the company in-depth knowledge inexpensively because the person responsible is a company employee. The company can use predictive modeling to identify which market segments are most profitable. The pool data generated by statistics can give a clear view of its customer base, optimize supply chains, determine the impact of unexpected events and simulate alternatives.

Descriptive and inferential statistics are principal assets in managerial decision-making. The essence of using descriptive statistics is to prove arguments to make effective and informed decisions. It is a way for the management to deduce the connections between variables essential in creating superior products and services. Although most companies neglect the importance of relying on descriptive and inferential statistics in decision-making, it can be a significant source of competitive advantage.

References

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine4(1), 60. https://doi.org/10.4103/ijam.ijam_7_18

Moore, M. H. (2000). Managing for value: Organizational strategy in for-profit, nonprofit, and governmental organizations. Nonprofit and Voluntary Sector Quarterly29(1_suppl), 183-204. https://doi.org/10.1177/0899764000291s009

Sauers, D. G. (1983). Using statistics to improve the competitive position of a small business. American Journal of Small Business8(1), 52-62. https://doi.org/10.1177/104225878300800111