Application of Statistical Concepts in Decision Making and Problem Solving

The complexity of any business undertaking makes it necessary to formulate relevant decision-making and problem-solving tactics. Management of uncertainty is critical in any business organization despite the lots of available data handling required. Statistics provide a way of dealing with these uncertainties by providing immediate answers to any problems. This eventually results in informed decisions as well as outstanding leadership to the subordinates. Empirical statistical and probability analysis provides a convenient medium for laying out business strategies as well as providing real-time solutions to business problems.

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Proper data analysis and interpretation can help justify decisions made by business managers. Strategies adopted outline some procedures that are high yielding and eliminate uncertainties in the production, as production fails are rare. In addition, high-quality products obtained are barely sensitive to environmental changes and variations in internal composition are minimal. Statistical strategies provide a significant tool that eliminates hindrances in the production process enabling high quality and improving productivity (Grover, 2016). A proper understanding of statistical variations is vital as it helps the managers to instill a culture of thinking statistically and approaching issues from a statistical line of thinking.

Probability, on the other hand, influences many businesses as it enables policy optimization and safe decision-making. A good understanding of uncertainty and probability models guides on decision making and eliminates possible losses in the production process as individuals can make the right decisions involving minimum risks. Profit optimization is solely dependent on the resource allocation procedure in business. Use of probability models in risk assessment helps in avoiding the loss-associated investments. Probability models generally act as a guideline to investors in making decisions and strategy formulations that optimize the profits from the scarce resources and enable them to thrive in the ever-competitive business world.

To generalize over an entire sample, this decision must result from a statistical analysis and proven valid to a given confidence level. The inferential statistical tests provide a way into making statistical decisions that solely depends on facts rather than imaginations. The test begins with a formulation of a null hypothesis, which is proven true or false through a statistical test procedure (Nishishiba, 2014). The most commonly used hypothesis test is the t-test. The establishment of a critical region of the test statistic shows whether the hypothesis is true or not for a distribution making it possible to make a conclusion based on statistical facts.

The use of probability distributions enables scenario analysis in creating possibilities, which are theoretically different for a particular event. The distribution acts as indicators of a statistical likelihood of an event. To assess the probability of the occurrence of an event amidst a set of other probable events, the probability density function for a particular event of interest is determined. The event must occur randomly (Suppes, 1969). A business may establish scenarios on the possible outcomes of an investment. The probability distribution of the expectations acts as a guide in determining whether to proceed with the investment or not. The importance of statistical distributions in decision-making processes is paramount.

Central tendency measures in data analysis provide an important tool for concluding and hence proving useful in the decision-making process. They enable proper description and comparison of sets of data. Mean, median and the mode are the most used measures of central tendency. They give the point where a majority of the variables clusters. This provides a reliable parameter in predicting the probability of an event occurring near that point or at the extremes. This serves as an excellent guide in making decisions in any business venture, as it is easier to predict the outcome based on the past statistical facts.

Regression analysis and correlations are vastly used are establishing possible relationships that can exist on different sets of data. Understanding the existence or absence of any relationships among variables can help in data interpretation that can enable the making of profound business decisions (Shennan, 1990). Due to the high competitions and the high need for efficiency, institutions need to have a good understanding of the impacts of the decisions made as well as be able to formulate better policies. Sufficient data about business parameters such as employees, business performance, and sales enables statistical data analysis using this technique and the correlations established. For example, an investor would want to predict the effect of reducing the employees on the average sales made by the company.

The success of a given business in making informed decisions and laying out strategies for problem-solving relies on the proper understanding of the past business behaviors as dictated by a time series. Data collected over a period comprised of a time series (Nishishiba, 2014). This series serves as a forecast on the likelihood of an investment project yielding to the expectations of the investors. The performance trend guides in the decision making based on whether there is a probability of reaping from an investment or experiencing a loss.

By using market research as a tool for validation and decision-making, companies can fix all the problems and evade potential losses. For example, statistical conclusions come in handy in insurance companies in fraud detection. These companies generally deal with either medical, properties or insured vehicle claims. Algorithms or manual audits used in decision making assess whether a claim is viable or fraudulent. Manual examinations demand a lot of time as well as resources to examine a given assertion. Statistical algorithms provide a less sophisticated approach to decision making. Suppose in an insurance firm, 5% of the claims presented are fraud. The probability of a complaint filed being fraud is predetermined. This makes it easier to draw a conclusion based on the facts presented to prove the claim viable.

Proper data analysis and interpretation present a convenient way of solving problems as well as in decision making in any firm. Understanding the correlation between the different statistical parameters provides a platform for predicting the future, reducing uncertainties, and taking calculated risks in a business.

References

Grover, J. (2016). The Manual of Strategic Economic Decision Making: Using Bayesian Belief Networks to Solve Complex Problems. Cham: Springer International Publishing : Imprint: Springer.

Nishishiba, M. (2014). Research methods and statistics for public and nonprofit administrators: a practical guide. Los Angeles: SAGE.

Shennan, S. (1990). Quantifying archaeology. Edinburgh: Edinburgh University Press: Academic Press.

Suppes, P. (1969). Studies in the methodology and foundations of science: selected papers from 1951 to 1969. (Vol. Synthese library). Dordrecht: D. Reidel.