example of statistics essay

example of statistics essay

The Importance of Statistics in Modern Society

1. Introduction to Statistics

These questions will be addressed in the different chapters of this guide to statistical analysis.

What do they find? Can I use their results in the context of my company, or should there be a difference? What are we missing in our current results that the final model should have? What are the permits, laws, and rights that you need to consider when getting the data, creating the model, and getting the results from the model?

You can only choose one, two, three, 95%, or 99%. What are my assumptions about the test? Is my final test robust? What other teams do that have the score of interest to me?

Does the model make sense? Do you evaluate the model using the best possible performance method? What are the statistical tests that you have to understand?

What do you do to address missing data? What kind of variable are you trying to obtain? Can you convert that to a numerical variable or a categorical variable? What kind of descriptive statistics should we use to address our situation? Are we getting the shape that is familiar to me in those descriptive statistics? Are we describing our groups separately or as global groups? What are the statistical methods that are out there to help you actually get to the answer for your question? What are the keys drivers that will impact our question and the statistical models? What models should you use to improve your categorical variable?

What is the question that you are trying to answer that would help you with your decision? Which data do you collect? What is the right element of the population that you have to be looking at? What is my sample size? Should you disaggregate your groups even more, or is there a group that really will not have an impact?

Statistics is the science of learning from data. It is about concepts and methods for identifying, drawing conclusions, and solving problems. Statistics by itself is of little value; it is the application of statistics that brings value to the decision maker. Below are a series of questions that may help us speed up the understanding and clarifying of the relevance of the topics.

1.1 Statistics

2. Applications of Statistics in Various Fields

Therefore, students who take statistics courses appear to be making a choice that will make them more meaningful employees and/or participants in society. They also appear likely to make themselves attractive employees – more likely to be able to benefit from employer-sponsored training or higher education as well as more likely to be able to work unsupervised. Given its importance to organizations, it is not surprising that a recent survey conducted for the membership of a major human resources management organization showed that employers rated educational background in statistics and communication as most important in identifying effective employees or potential effective employees.

In addition to these applications of statistical techniques to organizations’ pressing needs, a second importance of statistics to organizations is that a basic understanding of statistical concepts appears important to decision making in a complex world filled with conflicting demands. Greater utilization of sophisticated analyses and computerized analyses is associated with smarter decision makers and an ability to use multiple tastes and view different dimensions of issues from different angles. And far from being a profession used only by academicians, statistical consulting and statistical methodology research assist understanding, progress, and quality in almost every field.

Statistics can help provide real-world answers to important problems for a variety of fields, including the natural and social sciences as well as various advocacy and business organizations. Such organizations also use computer simulation models, comparison shopping studies, studies of the measurement properties of survey questions (i.e., question evaluation studies), labor market studies, employer hiring and screening research, studies of the economic viability of rental developments, and scheduling studies.

3. Challenges and Limitations in Statistical Analysis

Challenges are the following: primarily statistical, but often decision-centric. Models fail to provide valid, interpretable results. Personal intuition and simple heuristics are often not sound guides. Characteristics of data – presence of multiple levels, timelines, missing values, outliers, and limited sample sizes – make it difficult to rely on the experienced personal executive. Arbitrary, unjustified, or misleading decisions are made compared to the model implications. Conclusions based on inadequate statistical foundations. Outside pressures and biases lead to procedures that may be unjust, if not immoral. Practitioner’s vulnerability to criticism when mutation analysis reveals the inadequacy of assumptions behind a seemingly successful concept.

Limitations in statistical analysis arise from misunderstood data collection methods, properties of analyzed data, and the relationship between the underlying process and available data. Unsubstantiated assumptions and errors included in the model used to analyze data, as well as unrealistic expectations about the performance of the models built on the basis of this analysis, can also contribute to limitations. Particularly with large datasets and the number of potential models, a building block methodology may be applied to alter data to support a pre-conceived purpose. It underscores the essential role of statistics not only for guiding the development of new ideas but for providing a rigorous framework in which these ideas are implemented, tested, and put to use. These considerations suggest the motivation for the present volume to emphasize the developed models and to provide and apply a comprehensive statistical toolkit for model assessment and accuracy testing.

4. Emerging Trends and Technologies in Statistics

There is no one-size-fits-all statistical solution for data analysis, exploratory analysis, or model building. Basis expansion and orthogonalization remain fundamental concepts that are forgotten in mixed-method statistical paradigms. A data analysis strategy is needed to harness the statistical power. Data mining is a great exploratory exercise but a poor confirmatory scientific approach. For example, data mining of a large complex pharmacokinetic data set can be very informative for generation of new research questions or hypotheses. However, mechanistic modeling verification or refutation should never be based solely on data mining. The insights gained by going through the scientific method are often most meaningful and rewarding. The same should be true for complex systems from other application disciplines. The growing tree populating the statistics forest can be bioluminescent with the provision of prescient lightning coverage. The truly influential spark illuminates the knowledge pathway as it strikes the heartwood of the data summary to illuminate our understanding.

The underlying basic skills needed for a statistical career are problem solving, analytical skills, visualization, and open-ended research questions. However, new technologies often come and go and their application or delivery mode may change. Despite the hype, new data mining software can only find univariate summaries and not causal explanations or efficient predictions. There is a severe need for the seamless integration of the best methods from statistical and computer science to facilitate the modeling of complex systems in a wide range of application disciplines. Current technological trends and challenges from the specific field context are reviewed to highlight the opportunities and advances in statistical theory and methods. The field may be overwhelmed by the deluge of big and complex data in terms of data sources, information content, or structural behavior. Novel and innovative statistical approaches and methods are needed for adaptation in order to sift through the information slag to extract the knowledge gold.