Unit 5: Sampling Distributions

Sampling variability, the Central Limit Theorem, and sampling distributions of means and proportions

This section contains 4 topics with 0 subtopics, 14 practice problems and 36 flashcards. Each topic includes comprehensive written notes, worked examples with detailed solutions, and interactive lessons for hands-on practice.

Topics Covered

• Sampling Distributions — Understand sampling distributions and the variability of sample statistics.
• Central Limit Theorem — Apply the Central Limit Theorem to approximate sampling distributions as Normal.
• Sampling Distribution of the Sample Mean — Properties of the sampling distribution of x̄: mean μ, standard error σ/√n, and shape via the Central Limit Theorem.
• Sampling Distribution of the Sample Proportion — Properties of the sampling distribution of p̂: mean p, standard error √(p(1-p)/n), Large Counts condition, and normal approximation.

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