Sampling 02: Simple Random Sampling

Chapter 1: Simple Random Sampling

• Definition: Simple random sampling ensures that every sample has an equal probability of being selected, and each individual has the same chance of being chosen.

  • Example Scenario: A sample size of five individuals from a larger population.

  • Key Feature: All individuals have equal chance (likelihood) of selection.

• Equal Probability of Selection:

  • If a group consists of five women and five men, flipping a coin determines the entire sample group.

  • Coin Flip Outcomes:

    • Heads: Sample consists only of women.

    • Tails: Sample consists only of men.

  • Probability Analysis:

    • Probability of selecting any individual: 50%

    • Probability remains the same for each person regardless of gender.

Chapter 2: Evaluating Samples

• Probability of Samples:

  • Picking all women or all men both have a probability of 50%.

  • Picking a mixed sample (e.g., three women and two men) has a probability of 0% in this scenario.

  • Conclusion: Individual selection probability may be equal, but sample likelihood can vary (not all samples are equally likely).

• Proper Sampling Method:

  • It is more common to use a systematic approach for selecting simple random samples:

    • Assign everyone a random number.

    • Use random number generator to select individuals for the sample.

  • Example of selection:

    • Randomly select the included individuals through this numbered approach, ensuring true randomness in selection.

Chapter 3: Sampling Frame and Stratified Random Sampling

• Sampling Frame:

  • Definition: The complete list of individuals eligible for selection in a given study.

  • Example: The entire roster of eligible individuals for sampling.

• Next Steps:

  • Upcoming content will cover stratified random sampling, a different sampling method designed for population diversity.