Statistics in Business Statistics refers to the use of numerical information in everyday life to calculate facts and figures in limitless circumstances such as, batting averages, market share, and changes in the stock market. In addition, statistics refers to the scientific collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical data. Statistics involves describing data sets and drawing conclusions based on sampling about the data sets (McClave, Benson & Sincich, 2011). Statistics are divided into two areas: descriptive statistics and inferential statistics. Descriptive statistics are procedures used to describe and organize the basic characteristics of the data studied. Descriptive statistics provide simple summaries about the sample group and the measures. This application of statistics is used to present quantitative data in manageable forms such as charts, graphs, or averages. Descriptive statistics differ from inferential statistics in that they are simply describing what the data indicates. Inferential statistics are used to draw conclusi...
After reading this book, I am touched by the underlying philosophy of statistics. Various theories and models are introduced in this book. During the progress of the development, controversies and confits among these theories are largely attributable to diversity of ideology and doctrine from their establishers. In future, the statistics might evolve into a new era and the vogue methods, like p-value or confidence interval, might be discarded. I am looking forward to witness how statistics make our lives
Furthermore, the methods applied convey “the techniques or procedures used to gather and analyze data that is
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
Descriptive statistics is a procedure of organizing sample data. This procedure allows readers to be able to understand and describe the data’s importance. Descriptive statistics allows an individual to quickly understand the data and make predict an individual score; however, descriptive statistics does not describe all data in the sample. Inferential statistics is a process that determines whether sample data accurately represented the relationship to the population. In other words, one uses inferential statistics to determine if the sample data is believable.
Trochim, William M.K. "Descriptive Statistics." Descriptive Statistics. Web Center for Social Reseach Methods, 20 Oct. 2006. Web. 28 Apr. 2014. .
For people who are not statisticians, they may wonder what statisticians do, and how statistics could be applied in daily life. Statistics: A Guide to the Unknown is a supplementary reading materials designed for general readers even if he or she did not learn enough knowledge of statistics, mathematics and probability. Besides, it could give statisticians a general understanding of the important role of statistics in society. This book also analyzes how statistics assists people to gain useful information from massive data sets. In order to form a more respected book, the editors invite many distinguished researchers in statistics as authors. The book consists of twenty-five essays from different fields, including public policy and social science, science and technology, biology and medicine, business and industry, and hobbies and recreation. Each essay provides readers a description of how statistical methods are applied to solve issues in that field.
Broadly, statistics is a set of disciplines for study quantitative information. Implied that several methods used to collect or process or interpret quantitative data from large amount of information, then finally generate a calculated number, for example average, mean, standard deviation…etc. All of these are the key reference for decision making or predicting consequences. Thus, it enables us to estimate the extent of our errors.
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
What is descriptive statistics? Usually under descriptive statistics summative methods of description the data in succinct ways is considered. Data analysis usually begin with descriptive statistics, because it helps to understand what data we have – what is the sample, what is the accuracy of the data and how it is possible manage it.
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
Probability and Statistics most widespread use is in the arena of gambling. Gambling is big all over the world and lots of money is won and lost with their aid. In horse racing especially the statistics of a horse in terms of its physical condition and winning history sway numbers of persons into believing that the mathematical evidence that is derived can actually be a good indicator of a race’s outcome. Usually it is if the odds or probability are great in favor of the desired outcome. However the future is uncertain and races can turn out any of a number of different ways.
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.
Quantitative research involves the collection and converting of data into numerical form to enable statistical calculations be made and conclusions drawn. It provides a measure of how people think, feel or behave and uses the statistical analysis to determine the results. However, this measurement results in numbers, or data, being collected, which is then analyzed by using quantitative research methods (Byrne, 2007).