Statistics and management sciences

Statistics is “The mathematics of the collection, organization, and interpretation of numerical data, especially the analysis of population characteristics by inference from sampling.”
(The American Heritage® Dictionary)

“The collection, presentation, analysis and interpretation of the numerical data. The facts which are dealt with must be capable of numerical expressions.”

“Statistics is a discipline which is concerned with designing experiments and other data collection, summarizing information to aid understanding, drawing conclusions from data, and estimating the present or predicting the future.”
(The University of Melbourne)

The word statistics originated from Latin word “status” meaning “state”. Earlier it was identified solely with displays of data and charts pertaining to the economic, demographic and political situations prevailing in a country. Even today a major segment of the general public thinks this way. However gigantic advances during the twentieth century have enabled statistics to grow and has made it important as a discipline of database reasoning.

Management science (MS) is the discipline of using mathematical modeling and other analytical methods, to help make better business management decisions.

A management science is the application of scientific techniques that enable managers to make better decisions. Good decision-making can enhance the efficiency, productivity and profits of business firms. Management Sciences techniques are applied in many business functional areas, including supply chain and logistics management, inventory control, quality control, operations planning, production scheduling, sales forecasting, financial management, enterprise data mining, and customer relationship management. For instance, mathematical models can be used to create dependable flight schedules and crew shifts for airlines. Quantitative techniques can be used in deciding on service centre location, controlling production, or implementing statistical quality control in manufacturing companies. These techniques can also be used by service organizations such as banks, hospitals and investment firms to increase productivity, to provide better services to their customers and to increase profits. In this era of technological advancement in information technology, management sciences makes extensive uses of information technology to effectively model and solve large and complex management problems.


The statistics plays a very crucial role in the field of management science. It is practically in practice in the organizations. The management’s main responsibility is to make logically sound decisions and the base of the decisions is the data that depends on the work and the statistical tools. So the statistical is inevitable for the business.

Role of statistics in management sciences is discussed below:


Every manager operating in business environment requires as much information as possible about the characteristics of that environment. The most important thing is that majority of information required is of numerical nature for example interest rates, stock market prices, money supply, market demand strength, an auditor’s concern about number and size of errors found in account receivable etc. These information in raw form are impossible to comprehend fully.

Statistician’s role involves the extraction and synthesis of the important features of a large body of numerical information. One objective is to give sense to numerical data by summarizing in such a way that a clearly understandable picture emerges.
At some places simple and straightforward numerical or graphical summary is sufficient whereas at times you need to employ heavy artillery of formal techniques to provide a good basis for deeper analysis.


The world marketplace has faced immense competition over the past few decades. An international revolution in quality and productivity improvement has heightened pressure on economies. In order to survive the need is to mobilize workforce for continuous commitment to quality improvement. Improvement is possible knowing the current standards of quality provided and surveying what is required. At this stage, statistical skills in the collection and presentation of summaries are required.


The public is constantly bombarded with commercials that claim the superiority of one product brand in comparison to others. When such comparisons are founded on sound experimental evidence, they serve to educate the consumers. Not infrequently however, misleading advertising claims are made due to insufficient experimentation, faulty analysis of data, or even blatant manipulation of experimental results. Government agencies and consumer groups must be prepared to verify the comparative quality of products by using adequate data collection procedures and proper methods of statistical analysis.


Statistics does not deal with question of WHAT IS but of WHAT COULD BE or WHAT PROBABLY IS. At a time when assertions are made its not possible to tell surely of what is going to be the truth. This means that there is element of uncertainty. For example:

• The price of IBM stock will be higher in six months than it is right now.
• If the federal budget deficit is high as predicted, interest rates will remain high for rest of the years.

Although an analyst may believe that anticipated developments over the next few months are such that price of IBM stock is likely to rise over six months period, he or she will not be certain of this. This means that this possibility exists but to what extent or how much more likely to rise? This is answered with the help of statistical probability tools.


Before bringing new product in market, a manufacturer wants to arrive at some assessment of the likely level of demand, and a market research survey may be undertaken. He wants to know about potential population of all buyers. However, it is prohibitively expensive, if not impossible, for a typical market research survey to contact every member of that population. Rather a small sample of population members will be contacted, and any conclusions about the population will be based on information obtained from sample.

The technique of sampling large population is largely used in business. For example decisions about whether a production process is operating correctly are based on the quality of a sample from its output.

When we have information on a sample from population, it is generally straightforward to summarize the numerical sample data. However taking a sample is merely a means to an end. The objective is not to make statements about the sample but, rather, to draw conclusion about the wider population. Thus an important problem for statisticians involves the extent to which it is possible to generalize about a population, based on results obtained from a sample.


Statistics studies possibility and nature of a relationship between two or more variables of interest. For example, what would be effect of 5% increase in price on demand of automobiles? Economics says other things remaining same an increase in price brings decrease in demand. This theory is qualitative. It does not tell us how much demand will fall. Now we must collect quantitative information in order to assess how demand has responded to price changes in the past. Now we will base our assessment on premise that what happened in past is likely to be repeated after proposed current price increase. Objective of using numerical information is to learn something about the relationship between the variables of interest. Procedures for analyzing relationships are possible only through statistics.


Reliable predictions are needed in business. Investment decisions must be made well ahead of time at which a new product can be brought to market, forecasts of likely market conditions some years into the future is desirable. For established products, short term sales forecasts are important in setting of inventory levels and production schedules.
In forming economic policy, the government requires forecasts of likely outcomes for variables such as gross domestic product, unemployment, and inflation etc.

Forecasts of future values are obtained through the discovery of regularities in past behavior. Thus data are collected on past behavior of the variable to be predicted, and on the behavior of other related variables. The analysis of this information may then suggest likely future trends using statistical tools.


The tools and techniques that compromise business statistics include those specially designed to describe data such as charts, graphs and numerical measures. Also included are inferential tools that help decision makers draw inferences from a set of data. Inferential tools include estimation and hypothesis testing. The inferential tools includes two things the estimation and hypothesis testing.


In situations where one would like to know about all the data in large data sets but it is impractical to work with all the data, decision makers can use techniques to estimate what the larger data set looks like. The estimates are formed by looking closely at a subset of the larger data set.


Once the decision maker identified the important variables in a situation and established the relationship among them through logical reasoning in the theoretical framework the decision maker is in a better position to test whether the relationship that have been theorized do in fact hold true. By testing these relationships scientifically through appropriate statistical analysis or through negative case analysis in qualitative research we are able to obtain reliable information on what kind of relationships exist among the variables operating in the problem situation. The results of these tests offer the decision maker some clues as to what could be changed in the situation to solve the problem. Formulating such testable statements is called hypothesis development.


The results is interpreted in the light of the limitations of the original material. Too exact conclusions must not be drawn from data which themselves are but approximations. It is essential however, that the investigator discovers and clarify all the useful and applicable meaning which is present in its data.


Human beings cannot go through life without making mistakes, but should be reduced to a minimum. Factor of carelessness is eliminated by the use of statistics in business decisions by managers.


Companies are organized on basis of functions performed by them and that is why they are referred as functional organizations. Organizations perform three primary functions of finance, marketing and operations. Their secondary functions include maintaining accounting record, human resources management and information systems. Statistics help managers convert data into information which in turn plays a critical role in decision making.

Capital budgeting is the process by which a firm generates, analyzes and selects the projects that it will invest in. The most important issue addressed is composition of firm’s product line. Once a product is developed its financial feasibility is a must to be examined. Revenues and costs associated with the project are also important to be known. The probability concept is helpful in dealing with uncertainty surrounding the projected values of future cash flows.


Promotion is final component of marketing mix. A company must communicate with consumers to inform them about the company and its product and make them buy the product. Promotional tools available to achieve these objectives include advertising, public relations, sales promotion and personal selling. Statistical methods can be used to help assess how successful these tools have been in generating sales.


A critical decision for any firm is where to locate its production facility, storage center or retail outlet. The circumstances that effect location decision depend on type or facility. A variety of statistical techniques can be employed to help make the decision of choosing appropriate location as per requirements of business.


Project evaluation and review techniques and critical path method are management science procedures that help control and plan large scale projects. Probability distributions such as the normal distribution are applied in this topic.


People related decisions by a firm are dealt in functional area of Human Resource Management. It involves activities such as recruitment, training, performance appraisal, compensation and motivation. The HRM must forecast its needs in terms of number of employees required and their skills. If new staff is required then tests must be conducted to employ them on merit basis. The high scorers must be given priority over low scorers. Statistical tools can be used to assess the validity of these tests. Analysis of test results can also help to reveal deficiencies in the training programs. Periodic performance appraisal is important for making decisions regarding retention, compensation and promotion. Statistical methods can be used to assess the compensation program to determine whether it supports performance objectives.


An index number is a statistical device designed to show changes in a variable or a group of related variables with respect to time or geographical location such as wages, income, prices, exports or imports etc over a period of time. Today it’s the most widely used statistical device. They are the indicators of inflationary or deflationary tendencies. Industrial production rising or falling, sales are higher or lower than the previous period are all disclosed by index number.


From the above mentioned heads, it is concluded that statistics plays a key role in the field of management sciences.