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Main Sampling Methods in AP Statistics Sampling methods are techniques used to select a subset (sample) from a population to draw conclusions about the entire group. The goal is to obtain a representative sample to make valid inferences. Common Sampling Methods Simple Random Sample (SRS): - Every individual has an equal chance of being selected.

AP Statistics

What are the main sampling methods in AP Statistics?

Main Sampling Methods in AP Statistics

Sampling methods are techniques used to select a subset (sample) from a population to draw conclusions about the entire group. The goal is to obtain a representative sample to make valid inferences.

Common Sampling Methods

  1. Simple Random Sample (SRS):
    • Every individual has an equal chance of being selected.
    • Stratified Random Sample:
    • The population is divided into strata (groups) based on a characteristic, then a random sample is taken from each stratum.
    • Cluster Sample:
    • The population is divided into clusters (often geographically), some clusters are randomly selected, and all individuals in chosen clusters are sampled.
    • Systematic Sample:
    • Select every $k$th individual from a list after a random starting point.
    • Convenience Sample:
  • Individuals are chosen based on ease of access (not recommended due to bias).

  • Worked Example: Stratified Random Sample

    Problem:
    A school has 600 students: 400 in Grade 9 and 200 in Grade 10. You want a sample of 60 students, stratified by grade.

    Step 1: Calculate Proportions

  • Grade 9: $\frac{400}{600} = \frac{2}{3}$
  • Grade 10: $\frac{200}{600} = \frac{1}{3}$
  • Step 2: Allocate Sample Size

  • Grade 9: $60 times frac{2}{3} = 40$ - Grade 10: $60 times frac{1}{3} = 20$
  • Step 3: Randomly Select Within Strata

  • Randomly select 40 students from Grade 9 and 20 from Grade 10.

  • Takeaways

  • Sampling methods help ensure that a sample represents the population.
  • Stratified sampling improves accuracy when subgroups differ.
  • Avoid convenience samples, as they often lead to bias.
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