Statistics Foundation · Lesson 1.2
Populations, samples and variables.
This lesson goes deeper than Lesson 1.1. You will learn how to define a population precisely, judge whether a sample is useful, classify variables correctly, and recognise sampling problems before any calculation begins.
70–80 minute lesson plan
Learn how data becomes a study.
In Lesson 1.1, you learned that statistics turns data into evidence. This lesson explains where that data comes from. The quality of the population definition, sampling process and variable measurement determines how much we can trust the final conclusion.
0–10 min
Why populations matter
Understand that every statistical conclusion needs a clearly defined target group.
10–25 min
Samples as evidence
Learn why samples are used, why they vary, and why representativeness matters.
25–40 min
Variables and measurement
Distinguish numerical, categorical, binary, ordinal and time-to-event variables.
40–55 min
Sampling quality
Explore bias, random error, undercoverage, non-response and measurement error.
55–70 min
Worked examples
Apply the ideas to education, health, public surveys and research examples.
70–80 min
Practice and quiz
Write careful explanations and check your understanding with feedback.
Mastery checklist
By the end, you should be able to audit a dataset before analysis.
Define the target population precisely.
Separate target population from study population.
Explain what a sampling frame is.
Judge whether a sample may be biased.
Classify common variable types.
Explain why measurement quality matters.