BSTAT – Module 1 Vocabulary

Vocabulary flashcards covering key terms and concepts from BSTAT Module 1, including definitions, branches, analytic goals, variables, levels of measurement, and real-world applications.

Statistics

The science of collecting, organizing, presenting, analyzing, and interpreting data.

Descriptive Statistics

Branch of statistics that summarizes and organizes data so they are easier to comprehend.

Inferential Statistics

Branch of statistics that draws conclusions about a population based on data from a sample.

Data

The values that variables can assume; raw facts collected for analysis.

Population

The entire set of elements or subjects under investigation in a study.

Sample

A subset of the population selected for analysis.

Variable

A measurable characteristic of a subject that can take on different values.

Data Set

A collection of related data values.

Qualitative Variable

A variable whose attributes are categories or qualities rather than numbers.

Quantitative Variable

A variable whose attributes are numerical counts or measurements.

Discrete Variable

A quantitative variable obtained by counting; assumes whole-number values only.

Continuous Variable

A quantitative variable obtained by measuring; values may include fractions or decimals.

Measurement

The process of assigning numbers to observations according to specific rules.

Nominal Level

Scale of measurement that classifies data into mutually exclusive, exhaustive categories with no inherent order.

Ordinal Level

Scale that classifies data into categories that can be ranked, though exact differences between ranks are unknown.

Interval Level

Measurement scale with ordered categories and equal intervals but no true zero (e.g., IQ, temperature °C).

Ratio Level

Measurement scale with ordered categories, equal intervals, and an absolute zero, allowing meaningful ratios (e.g., distance).

Central Tendency

A statistical measure that identifies the center or typical value of a data set (mean, median, mode).

Variance (in a group)

The extent to which individual values differ from the group’s average characteristic.

Difference Between Groups

Comparison to determine whether subgroups or separate groups vary on a trait of interest.

Relationship (within a group)

Statistical association between two or more variables in the same group.

Prediction

Using statistical or mathematical models to forecast future outcomes based on data.

Descriptive Analysis

Analysis limited to describing the particular group studied without generalizing beyond it.

Inferential Analysis

Analysis that applies findings from a sample to the larger population from which the sample was drawn.

Analytic Goals

Targets of data analysis such as central tendency, variance, differences, relationships, and prediction.

SMART Problem Statement

A research problem that is Specific, Measurable, Attainable, Realistic, and Time-bound.

Applications of Statistics

Uses in diverse fields like computer science, economics, sports, public health, education, and business.

Stochastic Model

Statistical model incorporating randomness and prior knowledge about the data.

Algorithmic Model

Computer-science approach that relies on predefined algorithms without prior data knowledge.

Data Mining

Process of discovering patterns and knowledge from large data sets using statistical methods.

Speech Recognition

Technology that converts spoken language into text, often using statistical models.

Image Analysis

Extraction of meaningful information from images via statistical techniques.

Recall Rate

Percentage of viewers who remember seeing a particular advertisement or stimulus.

Statistical Study

A formal investigation involving data collection, analysis, and interpretation to answer research questions.

Backbone of Research and Business

Role of statistics in providing foundational tools for evidence-based decision-making in studies and commerce.