BASIC CONCEPTS IN STATISTICS || MATHEMATICS IN THE MODERN WORLD

Statistics is defined as a collection of methods for planning experiments, obtaining, analyzing, interpreting data, and drawing conclusions. This involves gathering relevant information (collection), organizing data into tables, graphs, or charts (obtaining data), deducing relevant information to formulate numerical descriptions (analyzing), and deriving conclusions and making predictions from analyzed data (interpretation).

Key terms in statistics include 'data,' which are the values variables can assume. A 'variable' is a characteristic that is observable or measurable in a unit of the universe or population. 'Population' is the set of all possible values of variables, while a 'sample' is a subgroup or subset of the population.

Variables are classified into 'qualitative variables' (words or codes representing categories, e.g., gender, religion, marital status) and 'quantitative variables' (numbers representing an amount or count, e.g., height, weight, household size). Quantitative variables are further classified into 'discrete variables' (countable data, e.g., number of siblings) and 'continuous variables' (data that can assume all values between any two specific values, e.g., weight, height, body temperature).

There are four levels of measurement: 'nominal level' (data consisting of names, labels, or categories only, e.g., gender, civil status), 'ordinal level' (data arranged in some order, e.g., happiness index, educational attainment, ranking), 'interval level' (similar to ordinal but with meaningful amounts of differences between data, e.g., body temperature, IQ), and 'ratio level' (the highest level, with a meaningful absolute fixed zero point and allowing all arithmetic operations, e.g., number of siblings, weight, height).

The four basic methods of sampling are 'random sampling' (using chance or random numbers), 'systematic sampling' (numbering subjects and selecting every nth number), 'stratified sampling' (dividing the population into distinct groups and taking a simple random sample from each), and 'cluster sampling' (using intact groups called clusters).