Inferential Statistics • Lesson 3
Sampling Methods
Learn how researchers select observations from a population — and why the method of selection can determine whether the conclusion is trustworthy.
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Why the Sampling Method Matters
A large sample is not automatically a good sample. If the selection method systematically excludes part of the population, the estimate will be biased no matter how many observations you collect.
Learn by Seeing
Method → trust
Big biased sample → still wrong Small good sample → often reliable Selection method drives trustworthiness.
The how of selecting observations matters as much as the how many.
Biased method · large sample
Large but systematically wrong
Good method · smaller sample
Small but representative
Interactive Activity
Which single factor most directly determines whether a sample will be trustworthy?
Hint: It is not just the size of the sample.
Statistics Analyst Mindset
Selection is a design decision
Before you compute a single statistic, ask: how was this sample selected? If the answer is "we surveyed whoever was easiest to reach", the estimate is compromised before the analysis begins.
Define
What population are we studying?
Choose
Which method fits the question?
Check
Was the selection representative?
Infer
Only then compute the statistic.
Lesson Summary
What you learned
Every member has equal chance.
Select every kth observation.
Sample from each defined group.
Select some groups and study all inside them.
Select by ease of access — biased risk.
Selection drives trust, not just size.