Introduction of Statistics

Economics is the study of how people and society choose to employ scarce resources that could have alternative uses in order to produce various commodities that satisfy their wants.

Types of activities: Economic activities and Non-economic activities

Economic activities refer to those activities which are undertaken to earn a living or for monetary gain. There are three main economic activities:

 

• Consumption: It is an economic activity that deals with the use of goods and services for the satisfaction of human wants.
• Production: It refers to all activities which are undertaken to produce goods and services for the market or for generation of income.
• Distribution: It is that economic activity which studies how national income is distributed among the factors of production namely land, labour, capital and entrepreneurship.

Non-economic activities are those activities which are not concerned with the creation of money or wealth.

Important Terms:

• Consumer: is one who avails or consumes goods and services for the satisfaction of his wants.
• Producer: is one who produces goods and services for the generation of income.
• Service holder: is a person who works for some other person and gets paid in return in the form of wages or salary. For example: a teacher employed in a school.
• Service Provider: is a person who provides some kind of service to the other for a payment. For example: Lawyer, Doctor etc.

Meaning of Statistics: Statistics can be defined in two ways:

A. Statistics in Singular Sense (methods of statistical enquiry)

In the singular sense, statistics refers to the collection, organization, presentation, analysis and interpretation of numerical data.

1. Collection of data : It is the first step in a statistical enquiry. The technique of collection of data depends on the purpose of study. Data can be collected using primary or secondary data collection methods.
2. Organization of data: After collection of raw data, the data is organized or classified in a proper manner on the basis of construction such as discrete, individual or continuous series or on the basis of characteristics like time series.
3. Presentation of data: Data, once organized, is presented in some suitable manner such as tabular, graphical, diagrammatic or textual form. iv.
4. Analysis of data: Analysis is done with the help of mathematical techniques such as measures of central tendency, dispersion, correlation etc.
5. Interpretation of data: It is the last step in statistical methodology. It involves interpretation of the final statistical results from analysis and drawing conclusions from the enquiry.

 

B. Statistics in the Plural Sense (features of statistical data)

In the plural sense, statistics refers to aggregates of facts affected to a marked extent by multiplicity of causes, numerically expressed, collected in a systematic manner for a pre-determined purpose, estimated according to reasonable standards of accuracy and placed in relation to each other.

1. Aggregate of facts: Data to be called statistics must consist of aggregate of certain facts. A single and isolated fact or figure like, ‘Ram is 15 years old.’ is not statistics. For a data to be counted as statistics it must be in the form of a set or aggregate of certain facts such as a series relating to ages of 30 students in a class.
2. Affected by multiplicity of causes: Statistical data is affected to a marked extent by a multiplicity of causes. There are a variety of forces or factors operating on the facts and figures in an aggregate. The influence of any particular factor cannot be isolated easily. For example: Statistics of production of a crop like rice is affected by extent of rainfall, fertilizer, seeds etc. It is not possible to study the effect of each of these factors separately on the production of rice.
3. Numerically expressed: Any fact to be called statistics has to be expressed numerically or quantitatively. Qualitative attributes such as honesty, truth, loyalty etc. cannot be called statistics unless assigned a numerical value as a quantitative measure of assessment. For e.g: ‘Ram is shorter than Shyam’ cannot be called statistics but if the same is expressed quantitatively in numbers like ‘Ram is 155 cm, Shyam is 160 cm and Anusha is 153 cm tall’, we can call it statistics.
4. Collected with a reasonable standard of accuracy: The standard of estimation and of accuracy differs from enquiry to enquiry or from purpose to purpose. There cannot be one standard of uniformity for all types of enquiries and for all purposes. The process of generalization can be achieved only with a reasonable standard of accuracy. For example: A single student cannot be ignored when we say that there were 50 students present in a class. But while reporting the number of people in a rally, the reporters merely find an estimate of the number of people.
5. Collected for a pre-determined purpose: Statistics should be collected for a pre-determined goal or objective in mind. Without any objective, data collected will be useless. Data collected without complete awareness of the purpose will be confusing and cannot be used for deriving valid conclusions. Thus, the purpose of collecting data must be decided in advance.
6. Collected in a systematic manner: For reliability or accuracy of data, the figures must be collected in a very systematic manner. Any rough and haphazard method of collection will not be desirable for that may lead to wrong conclusions and the reliability of such data could deteriorate.
7. Statistics should be placed in relation to each other: Collection of data is done for purpose of comparison of data. If the figures collected are not comparable, then they lose a large part of their significance. Also, data must be homogenous to make meaningful comparisons.

Functions of Statistics

1. To simplify complex facts: Statistical methods try to present huge complex numerical data into simple and understandable form. For example: Statistical techniques like mean, median, correlation etc. help condense huge data into a simple and easily understandable form.
2. To present facts in a definite form: Quantitative facts are easier to believe in comparison to qualitative facts. Statistics summarizes the generalized facts and presents them in a definite form. For example: Statement like ‘the annual rate of inflation is 6%‘ is more convincing and explanatory than a general statement like ‘Prices are rising’.
3. To make comparisons: Comparison between different sets of observation is an important function of statistics. Various statistical methods are used to compare data like averages, percentages, ratios etc.
4. To facilitate planning and policy formulation: On the basis of numerical data and their analysis, planners and businessmen can plan future activities and shape their policies.
5. To help in forecasting: Statistical tools like time series analysis and ‘what if’ analysis help in making projections for future. This helps businessmen make contingency plans for their future to reduce uncertainties arising out of the business cycles.
6. Formulating and testing hypothesis: Statistical methods can be extremely useful in formulating policies and testing hypothesis such as whether a rise in railway fares will reduce the passenger traffic or not.

Importance of Statistics

➢ In Business

1. For establishing a business unit : Statistics provides guidelines which may prove to be useful in making key decisions about size of output, availability of inputs, size of market share etc.
2. For estimating demand for a product: Statistics helps in estimating present as well as future demand of the product.
3. For production planning: Careful production planning helps in minimizing losses on account of over or under production. It is essential for maintaining a balance between demand and supply.

 

➢ In Economic Planning/Government

1. Using statistics, it is possible to assess the amounts of various resources available in the economy and accordingly determine whether the specified rate of growth is sustainable or not.
2. Statistical analysis of data regarding an economy may reveal certain crucial areas such as increasing rate of inflation which may need immediate attention.
3. Index numbers, time series analysis are extensively used for minimum wage legislations and other policy formulations.

 

➢ In Economics

1. Formulation of economic laws: The laws of economics such as ‘Law of Demand’ and ‘Elasticity of Demand’ have been developed using generalizations of statistical data and principles.
2. Helps in establishing mathematical relations: Statistical methods can be used to determine relations between different economic variables like price of a commodity and the quantity demanded of a commodity.
3. Study various market structures: Statistical comparison of prices, costs, profits of firms can give an insight into the features of various market types like monopoly, oligopoly, perfect competition etc.

Limitations of Statistics

1. Statistics does not study qualitative phenomena: It can only be applied to those problems which can be stated and expressed quantitatively. Qualitative characteristics such as honesty, poverty, welfare, beauty etc. cannot be directly measured quantitatively.
2. Statistics does not deal with individual facts: It deals only with aggregate of facts and gives no importance to individual items. For example: Marks of one student does not constitute statistics but the average marks of a class of students have statistical relevance.
3. Statistics can be misused: Statistics can be misused by wrongly motivated persons as data can be manipulated to draw any type of conclusions. For example: Governments often manipulate poverty figures to show lesser number of people below the poverty line.
4. Statistical results are only true on an average: Unlike other natural sciences whose results are universally true, statistical laws are not always as accurate. Statistical estimates are only true on an average and not on an individual basis.
5. Statistical laws are not exact: Since statistical laws are based on probability, the inferences derived are often approximations and not exact like inferences based on scientific laws.

Distrust of Statistics

It refers to the lack of confidence in statistical methods and statements

Causes: Due to improper use of statistical tools by irresponsible persons having incomplete knowledge of statistical methods, use of unrealistic assumptions, deliberate misuse of statistics and ignoring limitations of statistics.

Statistical methods are no substitute for common sense

Statistical data should not be believed blindly as they can be misinterpreted or misused. The statistical data may involve personal bias or may be subjected to manipulations for one’s own selfish motives. Statistical data and methods are also subject to the errors committed by an investigator while surveying and collecting data. Thus, one should use his/her common sense while working with the statistical methods.

A classic example exhibiting this concept was that of a family of four persons (husband, wife and two children) who once set out to cross a river. The father knew the average depth of the river. So, he calculated the average height of his family members. Since the average height of his family members was greater than the average depth of the river, he thought they could cross safely. Consequently, some members of the family (children) drowned while crossing the river. This example proves that common sense must supersede statistical methods.

 

Summary

• Our wants are unlimited but the resources used in the production of goods that satisfy our wants are limited or scarce and have alternative uses. Scarcity is the root cause of all economic problems.
• Purchase of goods by consumers to satisfy their various needs is Consumption.
• Manufacture of goods by producers for the market or generation of income is Production.
• Division of the national income into wages, profit, rent and interest is Distribution.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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