4: Key terminology (population, sample, variable)

Key Terminology: Population, Sample, Variable

Understanding foundational terms is critical for statistical analysis in economics. These concepts structure how data is collected, analyzed, and interpreted.

1. Population
A population encompasses every individual, object, or event relevant to a research question. It represents the complete set of entities sharing a defined characteristic. For example:

• Economic context: All registered small businesses in a country (for a study on loan accessibility).
• Key insight: Studying an entire population ("census") is often impractical due to cost, time, or logistical constraints.

2. Sample
A sample is a manageable subset drawn from the population. Its purpose is to represent the population accurately, enabling feasible analysis. Crucial considerations include:

• Representativeness: A sample must reflect population characteristics (e.g., selecting diverse industries when studying business productivity).
• Sampling methods: Random sampling ensures each population member has an equal chance of selection, minimizing bias.
• Economic application: Surveying 1,000 households to estimate national consumer spending habits.

3. Variable
A variable is any measurable characteristic that varies across population units. Variables classify data for analysis:

• Types:
  • Quantitative: Numerical values (e.g., GDP growth rate, monthly income).
  • Categorical: Non-numerical groups (e.g., employment sector: agriculture/manufacturing/services).
• Economic relevance:
  • Dependent variable: Outcome being studied (e.g., inflation rate).
  • Independent variable: Factor hypothesized to influence the outcome (e.g., interest rates).

Interconnection in Economic Research
Consider analyzing unemployment:

1. Population: All working-age adults in a region.
2. Sample: 5,000 adults surveyed via random digit dialing.
3. Variables:
   • Dependent: Employment status (categorical: employed/unemployed).
   • Independent: Education level (categorical) or age (quantitative).

Common Pitfalls

• Sampling bias: Over-representing urban households in a rural poverty study.
• Variable misclassification: Treating ordinal data (e.g., income brackets) as nominal.