Introduction to Statistics

The full picture

Statistics is the science of collecting, organising, analysing, and interpreting numerical data so we can draw reliable conclusions. Think of it this way: if your school announces 'the average attendance this month was 87%', someone had to count hundreds of daily entries, add them up, and compute a meaningful single figure. That whole process — from raw roll-call data to a useful number the principal can act on — is statistics at work. In economics, we use this same process to answer bigger questions: Is poverty falling? Are prices rising too fast? Is a government scheme actually helping?

Economics and statistics are inseparable partners. An economist cannot just say 'people are getting poorer' — she must measure it. The National Statistical Office (NSO), formerly known as the NSSO, conducts large surveys of Indian households to estimate income, consumption, and employment. The Reserve Bank of India tracks the Consumer Price Index (CPI) — a statistical measure — to decide whether to raise or lower interest rates. Without statistics, economic policy would be little more than guesswork.

Statistics serves two main purposes. Descriptive statistics summarises data you already have — your mark sheet reduces hundreds of individual scores into a neat average. Inferential statistics goes further: it uses a carefully chosen sample to draw conclusions about a much larger group. When the NSO surveys around 1 lakh households, it is not just describing those families; it is inferring the living conditions of all 140 crore Indians. This is powerful — and it is why the quality of the sample matters enormously.

But statistics has real limits you must know. First, correlation is not causation. If mango sales and school absenteeism both rise in April, that does not mean mangoes cause students to bunk class — summer vacation explains both. Second, averages can hide the truth. If a street has five residents earning ₹5,000 a month and one earning ₹5 lakh, the 'average' income looks comfortable but masks deep inequality. Third, a biased sample poisons every conclusion: a survey that only calls landline numbers in Kerala will miss most young, low-income, and rural people. Data is only as honest as the method used to collect it.

The good news is that learning statistics teaches you to think critically about every claim you encounter — in news reports, company advertisements, political speeches, and your own future career. Whether you plan to become a CA, a business owner, a journalist, or a civil servant, you will constantly meet numbers that somebody wants you to believe. This chapter is your first step toward reading those numbers with confident, informed eyes.

An Indian example

Priya runs a small textile unit near Thrissur with 12 workers. In January, her monthly wage bill was ₹1,80,000 — an average of ₹15,000 per worker. One month later, she hired her sister as manager at ₹80,000 a month. Now her total wage bill is ₹2,60,000 for 13 people, giving a new mean of exactly ₹20,000. She proudly tells the bank: 'Average worker salary has jumped from ₹15,000 to ₹20,000.' The bank manager raises an eyebrow. He knows that the original 12 workers still earn ₹15,000 each — only the new average looks better because one high salary pulled it up. Priya has not lied, but she has used a descriptive statistic — the arithmetic mean — in a way that paints a misleading picture. This is exactly the kind of situation the SCERT lesson warns you about: statistics can describe reality accurately or disguise it, depending on which number you choose to report and why.

Common misconceptions to watch for

• Wrong belief: 'Statistics always tells the truth — if it's a number, it must be a fact.' Correction: A statistic is only as reliable as the data behind it and the method used to collect it. A survey that only reaches wealthy urban families cannot tell you anything accurate about rural poverty, no matter how large the sample appears.
• Wrong belief: 'If two things rise or fall together, one must be causing the other.' Correction: Correlation just means two things move together — it does not explain why. In Kerala, both coconut prices and tourist arrivals peak in winter; neither causes the other — the season drives both. Finding the true cause requires controlled analysis, not just noticing a pattern.
• Wrong belief: 'A bigger sample is always more accurate.' Correction: Size matters far less than representativeness. A sample of 500 households spread evenly across all districts, income levels, and age groups in Kerala will give a more truthful picture of the state than 50,000 responses collected only from users of one smartphone app.