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Sampling

As explained in Effective Data Analysis

Random selection is a sampling procedure in which participants or subjects are chosen from a population using chance-based selection.

Sampling selects participants or subjects from a population using chance. In experiments, random selection helps connect observed results to the population being studied. section 3.2.3

Choose a selection approach that fits the population and experimental design:

  • Use pure random selection when every participant can be chosen directly by chance. section 3.2.3
  • Use stratified selection to choose participants in proportion to relevant subgroups. section 3.2.3
  • Use clustered selection by dividing participants into groups and randomly selecting at the group level. section 3.2.3
  • Keep sampling distinct from random group assignment, which places selected participants into treatment or control groups. section 3.2.3

Sampling is one condition supporting causal experiments, alongside random group assignment, controlled conditions, and direct manipulation of the independent variable. section 3.2.3

How Effective Data Analysis covers it

How deep it goes
Defines pure, stratified, and clustered selection, but does not detail implementation or sample-size decisions.
Examples
Uses experimental participants, population subgroups, and randomly selected groups as concrete selection examples.
What it assumes
Assumes familiarity with populations and experiments while distinguishing selection from later group assignment.
What you can do after
Apply pure, stratified, or clustered selection when designing an experiment.
How it connects
Connects random selection with random group assignment, controlled environments, independent variables, and dependent variables.
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