Understanding Sampling Methods in Statistics

learning_notes

Last updated: 8/16/2025

Key Concepts:

  • Random Sampling: A technique in which every member of a population has an equal chance of being selected, ensuring a representative sample.
  • Population vs. Sample: The population includes all members of a group being studied, while a sample is a smaller subset selected from the population.
  • Impact of Sample Selection on Generalization: If a sample is randomly selected, results can be generalized to the entire population; if biased, conclusions may not be valid beyond the sample.

Skills Covered:

  • Identifying random vs. non-random sampling.
  • Determining whether a study’s results can be generalized based on sampling methods.

Example Problem:

A researcher selects a group of 50 students at random from a university with 5,000 students to study study habits. To which group can the findings be generalized?

  • A. The 50 students in the sample
  • B. All students at the university
  • C. Only students with similar study habits
  • D. Students in the researcher’s class
  • (Correct answer: B, because the sample was randomly selected from the entire university.)

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