6.1 Data, sampling, probability and presentation notes

1. 6.1.1Statistics: meaning and scope; primary and secondary data; census and sample; schedule and questionnaire

   Statistics is the science of collecting, presenting, analysing and interpreting numerical data; data are primary (collected first-hand for the purpose) or secondary (already collected by someone else), and may come from a census (every unit) or a sample (a part).

   Advanced · 10 min · Read here
2. 6.1.2Sampling methods: random (simple, stratified, systematic, cluster) and non-random; sampling and non-sampling errors

   A random (probability) sample gives every unit a known chance of selection (simple, stratified, systematic, cluster); a non-random sample is picked by judgement or convenience; the gap between a sample result and the true value is the sampling error, while mistakes in collecting and processing are non-sampling errors.

   Advanced · 9 min · Read here
3. 6.1.3Probability: classical, relative-frequency and axiomatic definitions; addition and multiplication theorems

   Probability measures the chance of an event on a scale from 0 (impossible) to 1 (certain); it is defined classically (favourable ÷ equally likely cases), by long-run relative frequency, or by Kolmogorov's axioms, and is combined with the addition (or) and multiplication (and) theorems.

   Advanced · 7 min · Read here
4. 6.1.4Classification, frequency distribution and tabulation; diagrams and graphs (bar, pie, histogram, frequency polygon, ogive)

   Raw data are classified into groups, condensed into a frequency distribution, set out in tables, and shown by diagrams (bars, pies, pictograms) for comparison or graphs (histogram, frequency polygon, ogive) for frequency distributions.

   Advanced · 8 min · Read here

Medha AI is an independent preparation service. It is not affiliated with, endorsed by or connected to APPSC, TSPSC, MPSC, UPSC or any other commission or government body.