Introduction to Statistics

Management > Managerial Statistics > Introduction to Statistics

What is a Statistic?

• Statistics has been defined differently by different authors and each author has assigned new limits to the field which should be included in the scope of statistics
• Seligman, defines “It is a science which deals with the method of collecting, classifying, presenting, comparing and interpreting the numerical data to throw light on inquiry.
• Horace Secrist defines “It is the aggregate of facts affected to mark extent by the multiplicity of causes, numerically expressed, enumerated or estimated according to a reasonable standard of accuracy, collected in a systematic manner for the predetermined purpose and placed in relation to each other”.
• Prof. Boddington defined “It is the science of estimates and probabilities”.
• Croxton and Cowden define “ It is the science of collection, presentation, analysis, and interpretation of numerical data from logical analysis”.
• A.L. Bowley defines, “It may be called the science of counting and may be called the science of averages”.
• According to King, “It is the method of judging collective, natural or social, the phenomenon from the results obtained from the analysis or enumeration or collection of estimates”.
  We can see that the definition given by Boddington is complete and covers all characteristics of Statistics.

Components of Statistics:

• Collection of Data
• Presentation of Data
• Analysis of Data
• Interpretation of Data

Importance of Statistics in Business and Management:

Accounting:

• Statistical sampling techniques are used during the conduction of audits for clients. It also helps in detecting the trend and make a projection for next year.

Finance and Investments:

• Statistical information can be used to study the trend in securities and that can be used to provide investment recommendations. Statistical methods help in selecting securities which are safe and have the best prospects of yielding a good income.

Marketing:

• Statistical analysis is frequently used in for making a decision in the field of marketing it is the first step to find out what can be sold and to whom. Then using statistical methods a suitable strategy is formulated. A statistical analysis of data on production purchasing power, manpower, habits of competitors, habits of consumer, transportation cost can be done before entering a new market.
• Nowadays electronic scanners at retail checkout counters are used to collect data and to study the buying behavior of the customer. The data obtained in this procedure is used to analyze it to formulate future marketing policies.

Production:

• Statistical methods are used in quality control during the production process. It is also used to control and manage the flow of production. Statistical methods are used in the scheduling of men and machines.

Banking:

• Statistical data gathering and analysis of the information, help banks in their own business and also give an idea of the general economic situation of every segment of business in which they may have interest. Using this analysis they can formulate their lending policies.

Control:

• The management control process combines statistical and accounting method in making the overall budget for the coming year including sales, materials, labor and other costs and capital requirement.

Purchase:

• Purchase department can fix their schedule of purchasing orders depending upon the trends in consumption of raw materials and inputs. Thus they decide what to buy? When to buy? And how much to buy?

Economics:

• Statistical techniques and analysis are used for forecasting the future of the economy. Time series like moving averages, indicators like inflation index are statistical methods. We can consider statistics as the backbone of economics.

Categories of Statistics:

• Statistics is broadly categorized into two parts based on their functions a) Descriptive and b) Inferential.

Descriptive Statistics:

Descriptive Statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way.

• Descriptive Statistics involves collecting, organizing, summarizing, and presenting data.
• It is useful in clinical research while communicating the results with experiments.
• The methods used in statistics are preparing tables, drawing graphs, measuring central tendency, and the variation of the data from the central value.

Characteristics of Descriptive Statistics

• Descriptive statistics do not allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made.
• They are simply a way to describe our data.
• It enables us to present the data in a more meaningful way, which allows simpler interpretation of the data.
• There are two general types of statistic that are used to describe data: Measure of central tendency (mean, median, and mode), and Measure of spread (range, quartiles, absolute deviation, variance, and standard deviation).
• The data is represented by tables, graphs, and charts.
• This provides a quick method to make comparisons between different data sets and to spot the smallest and largest values and trends or changes over a period of time.

Limitation of Descriptive Statistics:

• They only allow us to make summations about the people or objects that we have actually measured.
• We cannot use the data we have collected to generalize to other people or objects. Thus no general inference can be obtained.

Inferential Statistics:

Inferential Statistics are techniques and methods that allow us to use samples to make generalizations about the populations from which the samples were drawn.

• Inferential Statistics involve making an inference, hypothesis testing, relation determination, and making predictions.
• The data obtained in descriptive statistics are analyzed and a valid inference is made out of it for effective decision making for managers and professionals. In this type deductions and conclusions are made regarding the population under study by collecting a sample from the population.

Characteristics of Inferential Statistics:

• They are produced through complex mathematical calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from it.
• Scientists use inferential statistics to examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population.
• Techniques that social scientists use to examine the relationships between variables, and thereby to create inferential statistics, include linear regression analyses, logistic regression analyses, ANOVA, correlation analyses, structural equation modeling, and survival analysis.
• Scientists used techniques like chi-square and t-test  which tell them the probability that the results of their analysis of the sample are representative of the population as a whole.
• Inferential statistics start with a sample and then generalizes to a population. This information about a population is not stated as a number. Instead, scientists express these parameters as a range of potential numbers, along with a degree of confidence.

Limitation of Inferential Statistics:

• It depends on the data provided (sample selected) and therefore, we cannot ever be completely sure that the values/statistics calculate are correct. There will always be a degree of uncertainty in this study.
• The inferential tests require the user to make educated guesses (based on theory) to run the inferential tests. Again, there will be some uncertainty in this process, which will have repercussions on the certainty of the results of some inferential statistics.

Hypothesis:

The idea you are testing is called the hypothesis. Statistics is used to confirm or refute some idea. Often in statistics, we confirm or refute the null hypothesis, denoted as H0. It is the hypothesis that essentially the results we get are random and are not due to some real relationship. In other words, if the null hypothesis is true, then the apparent relationship is really simply a random coincidence.

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Management > Managerial Statistics > Introduction to Statistics