Meaning, Functions and Limitations of Statistics

Every Andhra Pradesh Intermediate first-year Commerce student meets 'Statistics' in two senses in this course — once as a plural noun meaning numerical facts (statistics of a country's population, trade, or prices), and once as a singular science: the systematic method of collecting, classifying, presenting, analysing and interpreting numerical data so that sound economic conclusions can be drawn from them. This chapter is concerned mainly with the second sense — statistics as a tool an economics student uses to make raw numerical facts speak clearly, chiefly through diagrams, graphs, and measures such as the mean, median and mode.

Before any diagram or graph can be drawn, the numerical facts collected on a topic (called raw data) must first be arranged in a systematic order — classified into suitable groups and set out in a table (tabulation). Only after this preliminary step does statistics move on to the visual and numerical methods this chapter takes up.

Functions (importance) of statistics in economics:

1. It presents economic facts in a definite, precise numerical form rather than as vague general statements — 'unemployment is high' is an impression, '8.2% unemployment' is a statistical fact.
2. It condenses a large, unwieldy mass of data into a few meaningful figures (an average, a percentage, a diagram) that can be grasped at a glance.
3. It facilitates comparison — between two years, two regions, or two policies — through diagrams, index numbers and averages.
4. It helps in formulating and testing economic theories and hypotheses, for example, checking whether demand actually falls as price rises, using real market data.
5. It is indispensable in economic planning, budgeting, and forecasting, since every plan target and forecast rests on a numerical base built from past data.
6. It aids business and government decision-making by revealing trends, relationships, and the likely effect of a proposed policy change.

Limitations of statistics:

1. It deals only with facts that can be expressed numerically — qualities such as honesty, intelligence, or a policy's political acceptability fall outside its direct reach.
2. It studies aggregates and averages, not individuals — a statement about the 'average household income' says nothing about any one particular household.
3. Statistical conclusions (laws) are true only on average and in the long run — they are not universally true of every single case, unlike a law of physics.
4. Statistics can be, and sometimes is, misused or given a misleading interpretation by an unskilled or dishonest user — the same set of figures can be presented to support opposite conclusions.
5. Statistical methods are best applied to homogeneous data; mixing dissimilar items (say, adding the 'number of factories' to the 'number of workers') produces meaningless results.
6. Results are only as reliable as the data collected — an error at the collection stage carries through every later stage of analysis, however sound the method used afterward.