Introduction to Statistics – Key Vocabulary

Vocabulary flashcards covering the essential statistical terms introduced in the lecture, including types of statistics, sampling concepts, statistical inference tools, measurement scales, data types, and reasons for sampling.

Descriptive Statistics

Branch of statistics that summarizes and presents the main features of a dataset without drawing conclusions about a larger population.

Inferential (Explanatory) Statistics

Branch of statistics that draws conclusions, makes predictions, or tests hypotheses about a population using information from a sample.

Mean

The arithmetic average of a set of numbers; a measure of central tendency.

Median

The middle value in an ordered dataset; 50 % of observations lie above and 50 % below.

Standard Deviation

A measure of spread that indicates how far observations typically lie from the mean.

Frequency Table

A tabular summary showing how often each value or category occurs in a dataset.

Population

The complete set of experimental units or elements about which information is desired.

Experimental Unit

An individual object (person, animal, item, etc.) on which measurements are taken.

Variable

A characteristic or property of an experimental unit that can vary from one unit to another.

Sample

A subset of elements selected from a population for analysis; denoted by n, where n < N.

Representative Sample

A sample whose composition accurately reflects the structure of the population.

Random (A-select) Sample

A sample selected so that every population element has an equal chance of being chosen, without systematic bias.

Statistical Inference

The process of making estimates, predictions, or generalizations about a population based on a sample.

Confidence Measure

A quantitative statement about the uncertainty associated with a statistical inference (e.g., 95 % confidence).

Hypothesis Test

A formal procedure that uses sample data to decide whether to accept or reject a stated claim about a population.

Confidence Interval

A range of values, calculated from sample data, that is likely to contain the true population parameter with a specified confidence level.

Regression Analysis

A statistical technique that models and assesses the relationship between one dependent variable and one or more independent variables.

Measurement Scale

A system that defines how variables are categorized, ordered, and numerically scaled (nominal, ordinal, interval, ratio).

Nominal Scale

Categorical measurements with no intrinsic order (e.g., religion, gender); analyses include counts and mode.

Ordinal Scale

Categorical measurements with a meaningful order but unknown spacing between categories (e.g., military ranks).

Interval Scale

Numerical measurements with equal intervals but no true zero point (e.g., temperature in °C); allows mean and standard deviation.

Ratio Scale

Numerical measurements with equal intervals and an absolute zero (e.g., weight, age); all statistical operations are valid.

Qualitative Data

Non-numeric information classified into categories or groups (e.g., disease status, political party).

Quantitative Data

Numeric information measured on a naturally occurring scale (e.g., number of traffic fatalities).

Continuous Variable

A quantitative variable that can take any value within an interval (e.g., height, temperature).

Discrete Variable

A quantitative variable with a finite or countable number of possible values, typically whole numbers (e.g., number of students).

Sampling Reason – Large Population

Sampling is used when studying every element is impractical due to the population’s size (e.g., national census).

Sampling Reason – Destructive Measurement

Sampling avoids destroying the entire population when tests are destructive (e.g., estimating biomass by cutting plants).

Sampling Reason – Cost Constraints

Sampling reduces expenses when measuring the whole population would be too costly in time, travel, or technology.

Coding Qualitative Data

Assigning numerical codes to categorical responses to facilitate data entry and analysis (e.g., Religion: 1 = Catholic, 2 = Islam).