Statistical analysis plays a crucial role in the evaluation of the events that guarantee decision-making on the multiple alternatives available. The statistical analysis field entails the consolidation of data that provides a cause of interest or problem to the decision maker. This approach enables one to identify the useful data, design ways of analyzing it, present the findings, and finally recommend the best alternative. This implies that statistical analysis is valuable in analysis, planning, and decision-making. In this regard, a person in possession of the essential skills of decision-making will consider statistical analysis as the most appropriate approach when dealing with complex and varied alternatives. Additionally, it is possible to prove any decision made concerning statistical analysis and determine its true representation. Therefore, it is crucial to understand various statistical analysis segments, which guide a decision maker in realizing optimum decisions. However, the outcome of any decision made through statistical analysis may not always provide the best results. In most instances, it provides a guide to the best results.
In most cases, statistical analysis is critical in the evaluation of decisions based on their nature, but in conditions of uncertainty, it uses ideal methods to provide optimal solutions. Since statistical analysis provides the means by which the descriptive and inferential statistics are analyzed, decision-making becomes simplified and convenient even for a considerable amount of data. Descriptive statistics entails the numerical analysis of samples in order to obtain a comprehensible view of a wide population on what is under analysis. Inferential statistics refers to the use of samples to represent populations to forecast on the future expectations. This implies that through the process of statistical analysis, it is possible to realize appropriate decisions based on either past information or anticipation. Furthermore, statistical analysis provides the means through which descriptive and inferential statistics facilitate an effective and precise decision-making process.
Under descriptive statistics, the decision maker analyzes the information collected from past occurrences by using the available methods to summarize data in a shorter form. The output of this procedure enables the decision maker to unmistakably comprehend past occurrences and determine the most appropriate verdict. Similarly, inferential statistics attempts to explain processes and formulate the relevant predictions based on their understanding. This implies that it is vital to formulate a model that serves as a form of conceptual or mathematical representation. From this model, which is assumed correct with some errors accounted for, we infer the process under study. Concerning errors, statistical analysis provides ways through which a person can quantify the confidence of the inference made. In most cases, the determination of the validity of the null and alternative hypothesis under test is assumed significant to a certain level. Nevertheless, statistical analysis adopts varied techniques to analyze sample datasets collected from populations through sampling strategies such as describe, explore, understand, prove, and predict. This implies that it is possible to analyze any dataset from different dimensions, which provides the decision maker with a critical point of viewing the different outcomes to realize the best decision.
The technique of exploring or describing guides the decision maker in the evaluation of the dataset’s nature based on the linage or variables available. Such a model seeks to establish the relationship that exists between the dataset and the underlying populations. In addition, it outlines the suitable hypothesis and appropriate analytical techniques that will present the findings in graphical or numerical terms. In this regard, a graphical presentation assists the end users of the analysis to visualize since most people are proficient in observing patterns. Meanwhile, the outcomes’ numerical presentation will summarize the outstanding features of the analyzed data. Hence, decision makers can gauge the most appropriate initiative to undertake from the analysis presented.
The understanding technique attempts to represent the samples of an underlying process from which the data has been extracted. The outcome of the analyzed data will guide the decision maker to infer the nature of the process. In this regard, it would be essential to summarize this technique using a model. This model ought to be a mathematical representation of a process with a certain level of certainty and some set of acceptable assumptions. An example of such a model is an empirical model. A process model would depict the exact nature of the underlying process and try to simulate it. In this regard, the findings of the simulation process would provide a hint to the decision maker regarding an effective decision-making. At the same time, the model formulated will provide better means of selecting future decisions upon the identification of the correct parameters. This indicates that statistical analysis is useful for not only a single period, but also over a continuous period so long as it is a true representation of a problem.
The prove technique is another crucial approach to undertaking statistical analysis. This technique seeks to determine the nature of the presented statements so that the decision maker can conclude about the validity of such statements. For this technique to be effective, the formulation of a plausible model is essential. At the same time, the identification of the level of the model’s confidence is vital, as it will help the decision maker to ascertain its accumulated evidence. From this concept, the development of a model with reasonable assumptions for determining the sampling strategies that guarantees a certain level of confidence would be possible. Having realized the above requirements, testing of the model with the collected data to determine the outcome should be undertook. From such a model, the decision maker can confidently conclude on the matters that need prove.
The predict technique adopts a model that explains the findings of an underlying population from un-sampled entities. By using interpolation and extrapolation, the values of interest to the decision maker would be easily determined to assist in making appropriate decisions. Interpolation evaluates the values of a certain dataset within the original sample. On the other hand, extrapolation provides the findings of the values outside the range of the original sample. In this regard, the decision maker can use the model to ascertain any data that is valuable in making objective decisions.
For statistical inference, this approach is one of the guiding components in decision making concerning inference from a selected population. Usually, a sample true mean is calculated to determine the true value of the parameters of interest. Additionally, a degree of support is identified in support or in contradiction to the stated hypothesis to be validated. From the outcome of the analysis, it is possible to estimate the sample mean and the standard error of the parameters used in the model. These values will enable the user of the findings to critically evaluate the validated dataset and determine whether it is ideal for decision-making. In case the validity is suitable to the activities under test by the decision maker, an appropriate inference can be made from the model’s outcomes. Multiple statistical tools that enable the decision maker to ascertain the outcome from a statistical analysis guide this procedure.
Considering the significance of the process of decision-making, the models developed through statistical analysis guide people in making feasible decisions that will be objective to them. It involves varied ways of concluding for any chosen dataset, but the definite outcomes are usually similar for multiple methods. This indicates that although there might be contradictions in the varied methods available in statistical analysis, the guides in making inference or descriptive data for a sampled dataset, most exhibit their own strengths and weakness in every perspective of their usage. This implies that for any statistical model used, there must be a correlation between the data under analysis and the topic of the data. An adequate representation of the data by the appropriate model will guarantee optimal conclusions. In this regard, the statistical model would guide a decision maker appropriately. Nevertheless, the proper handling of the parameters and the chosen statistical model plays a significant role in ensuring that the statistical model contributes to decision making.
The systematic and analytical approaches depicted by statistical analysis assist the decision makers to attain appropriate decisions that seek the optimal solution for any problem of concern. Considering the manipulation of data by the statistical methods, these methods are ideal to provide guidance in the decision-making process. Any decision made base on these methods mostly portrays an optimal solution. At the same time, the fact that statistical analysis facilitates the handling of varied data enhances the realization of simple and precise conclusion that aid in decision-making despite these methods differences. Evidently, decision-making under conditions of uncertainty portrays statistical analysis as indispensable in the decision-making process. On the contrary, making decisions in situations of deterministic nature indicates that statistical analysis procedures are unsuitable.
Notably, the use of statistical analysis in decision-making exhibits multiple weaknesses, which may adversely affect the decisions made. Since the process of decision making demands decisions attained on accurate and objective findings from an analysis, incorrect misrepresentation of an analysis might lead to unsuitable decisions. Mostly, incorrect statistical analysis emanates from the incorrect identification of the model used for data analysis. This, therefore, would definitely provide inappropriate conclusions regarding the anticipated problem to under analysis. At some point, the errors exhibited by a model may not be plausible. Thus, inappropriate conclusions would be attained regardless of whether the model is accurate. Similarly, in the event that the parameters used for a statistical analysis contain errors, the conclusions would provide an incorrect representation of the problem under analysis. Thus, any errors that may result from the components of a statistical analysis imply that inappropriate decisions would be made in such a scenario. Additionally, from the fact that there are many assumptions that have to be made in order to use statistical methods, not all conclusions made from the findings are a true representation of the reality. Thus, a decision maker has to be knowledgeable on the weaknesses that these methods exhibit to provide an allowance on every decision made. At the same time, a decision maker could employ other means that guide in the decision-making process.
From the weaknesses depicted by the statistical analysis methods, decision makers are necessitated to be relative in their decision making process rather than the expected certainty. This indicates how decision makers feel deficient in the attainment of optimal solutions for various problems. As a result, modern approaches that guarantee better recommendations than the statistical methods are preferred to aid in the decision-making process. Nevertheless, statistical analysis exhibits the fundamental principles that assist in understanding the decision making process. Based on these finding, statistical analysis has both positive and negative effects on the decision-making process. Despite this finding, its positive effects outweigh the negative ones. This is indicated by the fact that statistical analysis is indispensable in decision making under the conditions of uncertainty.