Statistics in Economics Overview

Study Notes

Statistics are crucial in economics because they provide quantifiable data to analyze economic actions and behaviors.
Through statistics, researchers can: quantify, analyze, and interpret economic data, leading to informed decision-making.

Science of data: Statistics is the discipline of collecting, analyzing, and interpreting quantitative data.
Numerical insights: It uses numbers to provide insights, supporting decisions.

Alfred Marshall: Economics is the study of human behavior in everyday life.
Lionel Robbins: Economics examines human behavior as a relationship between limited resources and unlimited needs with alternative uses.

Plural Sense: Statistics refer to information presented in numerical form, for example, poverty statistics, employment statistics, or population statistics.
Data Examples: Data sets like unemployment rates, average income levels, and population growth rates all are examples of statistics.

Non-Statistical Numerical Information: Individual data points without aggregation or analysis do not constitute statistics.
Statistics vs. Single Data Point: For example, saying "Ayesha has $500 left in her wallet" is just a numerical fact, but "The average amount left in an individual's wallet is $500" is a statistic because it represents an aggregate measure.

Bowley: Statistics are numerical statements of facts pertaining to any area of inquiry. They present facts in relation to each other for better understanding.
Kendall and Yule: Statistics are quantitative data influenced by multiple causes, highlighting the complexity of various factors affecting the subject of inquiry.

Aggregate of Facts: Single numbers are not statistics, as meaningful conclusions cannot be drawn from them. Statistics are aggregates of facts, providing a basis for analysis.
Numerically Expressed: Statistics must be expressed in numbers. Qualitative descriptions like "small," "big," "poor," or "rich" are not statistics.
Multiplicity of Causes: Statistics are influenced by multiple factors. Even if one factor is removed, the remaining factors maintain the data’s meaning due to their complexity.
Reasonably Accurate: Statistics should be reasonably accurate to be reliable, ensuring informed decision-making.
Mutually Related and Comparable: Statistical data must be related and comparable for effective analysis and interpretation.
Pre-Determined Objective: Statistics are collected with a specific purpose, ensuring that the information gathered is relevant and answers the intended questions.