The document discusses key concepts related to sampling including the differences between populations and samples, probability and non-probability sampling, and the central limit theorem. It notes that samples are used to make inferences about populations and discusses factors like time, cost, and consumption that necessitate sampling rather than examining entire populations. The central limit theorem states that the distribution of sample means will approach a normal distribution as the sample size increases.

1. Revisiting Sampling Concepts

4. Population Sample Sample Statistics Population Parameters Inference Samples are drawn to infer something about population

10. Validity of Sampling Process

13. Distribution of the Population

14. Sampling distribution of means n = 2

15. = µ = 0.6 3 3,3 9 2 Mean of SD 2.5 3,2 8 2 3,1 7 2.5 2,3 6 2 2,2 5 1.5 2,1 4 2 1,3 3 1.5 1,2 2 1 1,1 1 Sample Mean Sample Sample #

24. 54 o -2.35 0.0094 52