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Managerial Decision-Making: Inferential and Descriptive Statistics

One of the major factors that affect business competence is the role of management in the decision-making process. On the one hand, it is the mandate of the administration to ensure that employees successfully attain the objectives while enhancing optimal satisfaction levels among the consumers. On the other hand, the executive contributes to the growth of an enterprise based on the necessity of interpreting the financial data gathered from the daily cash flow records. Researchers establish different tools that influence the interpretation and presentation of information pose dynamic attributes (Oboh & Ajibolade, 2017). This research seeks to assess the difference between inferential and descriptive analytical techniques while determining the integral value to the supervisor’s team and the duty to the corporation. The distinction in the examination of statistics is a phenomenon that profoundly empowers the leadership with strategic details for developing proficient approaches for the organization regionally and internationally.

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Differences between Inferential and Descriptive Statistics

There is a major distinction between both approaches due to the quality of information derived under a particular spectrum. In this case, data extraction is a crucial element for management during the decision-making to determine the essential factors affecting the performance and the key elements attributing to the competitive advantages. On the one hand, descriptive analysis deals with the explanation of the abstract details collected from a research exercise (D’Souza et al., 2017). On the other hand, inferential statistics concerns the establishment of critical insights regarding an entire population and its relation to a phenomenon being gauged. Therefore, the approach seeks to derive information regarding the particular sample population under assessment. Primarily, the perspectives focus on the representative components to consider the interplay across the dynamic values and interpretive framework for administrative duty in implementing organizational policies.

Both analytical approaches contribute to the information derived for different purposes based on the relevance and the necessity for specific points. However, it is important to note that there is higher certainty with descriptive statistics, while inferential analysis poses a percentage of uncertainty due to the measure of relationship across a larger representation. In this case, the latte focuses on enlarging the sample size to the actual population to reduce the marginal difference and enhance the confidence level during the assessment (D’Souza et al., 2017). It is vital to measure the distinction between the two variables under the spectrum of the scope of the segments. The primary duty of the management involves making critical decisions that affect the operations. Therefore, it is essential to integrate both perspectives in the derivation of details during research to determine the objective alignment of the interactions.

Application of Inferential and Descriptive Statistics in General Electric Company

General Electric is an international corporation producing an array of machinery and provides a variety of services. Excellent examples of the company’s products encompass industrial diamonds, jet engines, electrical equipment, and lighting goods. Besides, the firm offers various services such as installation, engineering, and repair of appliances. Despite the wide range of activities GE is engaged in, the association faces stiff competition, both inbound and outbound. The enterprise has affiliates in different fields, such as telecommunications and banking. Examples of these institutions include the NBC network and Capital Services. The company handles export and import within the United States and foreign countries (Wise, 2020). Consequently, General Electric has acquired a significant global market in the financial, manufacturing, broadcasting industries, and its competent affiliate companies continue contributing to its growth and market expansion.

The management decision approach is a phenomenon that significantly affected General Electric’s financial statements’ credibility by the Securities and Exchange Commission. Over the decades, the corporation optimized the administration’s decision based on historical experiences. The elevated assumptions in the determination of profits and losses fostered the incurrence of unbalanced records. An excellent example entails the GEC’s insurance accounting policy of the premium returns. According to the institution’s monetary policy, on a short-term basis, the insurance payments are reported as revenue already earned and rough estimates of the incurred expenses. In this case, the company’s financial statements project the profitability based on the approximations of earnings from premiums. The program regarding the short-term insurance quality contradicts records of sales and purchases after the actual transactions. The policy’s contrast poses a significant challenge in the financial statements due to the accuracy in balancing the incurred profit and loss (Berthelot et al., 2019). Securities and Exchange Commission handles cases such as the lack of impartiality and precision by imposing fines and the condition of hiring external auditors.

General Electric faces the core challenge in managerial decision-making hence the importance of incorporating both statistical perspectives in deriving details. On the one hand, the integral approach gears the provision of adequate information regarding the productivity and the operations in General Electric under the spectrum of descriptive analysis (D’Souza et al., 2017). On the other hand, the inferential aspect fosters the assessment of the relationship between products and services and the customer’s service experience.

Consequently, inferential and descriptive statistics play a vital role in managerial decision-making due to the dynamic derivation of information from the data collected. On the one hand, different companies utilize distinct approaches to extract crucial details that translate to competitive advantage initiatives. On the other hand, poor implementation of the perspectives risks the ineffective administration of a company to elevate its market acquisition techniques. It is important to empower the leadership with in-depth knowledge regarding an organization’s operations, such as General Electric, for efficient supervisory responsibilities for growth at regional and international levels.

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Berthelot, M. J., Lasensky, N., & Somers, P. (2019). The board’s role in monitoring strategy: Lessons learned from General Electric. Corporate Governance.

D’Souza, M. J., Brandenburg, E. A., Wentzien, D. E., Bautista, R. C., Nwogbaga, A. P., Miller, R. G., & Olsen, P. E. (2017). Descriptive and inferential statistics in undergraduate data science research projects. Advances in Statistical Methodologies and their Application to Real Problems, 10, 65721.

Oboh, C. S., & Ajibolade, S. O. (2017). Strategic management accounting and decision making: A survey of the Nigerian Banks. Future Business Journal, 3(2), 119-137.

Wise, G. (2020). Willis R. Whitney, General Electric and the origins of US industrial research. Plunkett Lake Press.

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