Module 3 · Lesson 5 of 17
Sampling strategies and bias
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Learning objectives
- Choose between random, systematic, stratified and time-based samples.
- Identify selection bias before it invalidates the study.
- Define a sample frame that matches the population of interest.
Sampling that represents the process
A sample estimates a population only when selection has no systematic bias. Stratify by style, size, colour, line, machine, operator, shift and lot when those factors matter.
A large convenient sample can be more misleading than a small honest one because size hides bias behind narrow confidence intervals.
Garment-factory example
Sampling only the top garments in a repaired bundle understates final defect risk. Stratifying by line × size × shift gives an honest picture of variation the buyer will actually see.
Method
- Define the population and list the important strata.
- Choose probability or rational sub-grouping.
- Calculate a practical sample size for the target precision.
- Log every missing observation and its reason.
Common mistakes
- Convenience sampling from the top of the pile.
- Assuming a large biased sample is reliable.
Knowledge check
Pick one answer per question. Explanations appear after you submit.
1. Stratified sampling protects a study by:
2. A large convenience sample is unsafe because:
Author: NOSK.life · Last reviewed: 2026-07-21