The document provides an overview of statistics, covering its definition as a branch of mathematics focused on data collection and analysis for decision-making. It differentiates between descriptive and inferential statistics, outlining examples of each, as well as key concepts such as population, sample, parameter, statistic, and data types (qualitative vs quantitative). Additionally, it distinguishes between primary and secondary data sources.

1. Statistics - Basic Definitions and Concepts
Statistics
A branch of mathematics that deals with the
collection, organization, presentation, analysis and
interpretation of data for decision-making.
Branches of Statistics
The following are the branches of statistics:
Descriptive Statistics
Methods and treatments used in the collection,
description, and analysis of numerical data.
Describes the population AS IT IS.
Examples:
 Monthly average sales
 Survey results
 Tally sheet of products in stock
 Bank records
 Number of beer bottles sold in a week
Inferential Statistics
Logical process of sample analysis in order to arrive
at a generalization or conclusion about a
population.
INFERS a generalization of the population using a
sample.
Examples:
 Quarterly sales projections
 Determining the effect of price inflation on
monthly profits
 Forecasting business growth for the next five
years
 Finding the relationship between geographic
location and customer preference
 Analyzing the influence of TV advertising on the
popularity of a candidate with voters
Population
All the members of the group to which a
characteristic applies.
Examples:
 All SSS contributions
 All Globe Postpaid subscribers
Sample
A portion or part of the population selected for
analysis.
Examples:
 Pulse Asia survey participants
 Focus groups
Parameter
Numerical value describing a characteristic of a
population.
Examples:
 Population mean (μ - Mu)
 Population variance (σ2
- Sigma squared)
 Population standard deviation (σ - Sigma)
Statistic
Numerical value describing a characteristic of a
sample.
Examples:
 Sample mean (x
̄ - x bar)
 Sample variance (s2
- s squared)
 Sample standard deviation (s)
Qualitative vs Quantitative Data
Qualitative Data
Data that are analyzed in terms of distinct
categories.
Data that can't be measured.
Examples:
 Educational attainment
 Sex
 Occupation
Quantitative Data
Data that are analyzed according to differing
numerical values or degrees.
Data that can be measured.
Examples:
 Monthly income
 Height
 General Weighted Average

2. Sources of Data
Primary Data
Data that comes from an original source.
Examples:
 Interviews
 Questionnaire answers
 Survey results
 Experiment findings
 Tally sheets
Secondary Data
Data that are taken from previously recorded
information.
Examples:
 Research findings
 Financial statements
 Government surveys
 News reports
 Historical accounts