Chapter 6 – Sampling and estimation. This chapter include objectives: Define simple random sampling, define and interpret sampling error, distinguish between time-series and cross-sectional data; state the central limit theorem and describe its importance, distinguish between a point estimate and a confidence interval estimate of a population parameter,. | Sampling and estimation Parameters and statistics A parameter is a quantity used to describe a population, and a statistic is a quantity computed from a sample and is used to estimate a population parameter and describe the sample. We typically use statistics to estimate parameters because It isn’t possible to examine the entire population. It isn’t feasible to examine the entire population (., too expensive). In order for a calculated statistic to convey information about the related population parameter, certain conditions must be met; those conditions are generally satisfied if the sample used to calculate the statistics is random. 2 Pages 215–216 Although it is not in the LOS, a reminder of the relationship between statistics and parameters as the basic foundation for sampling processes motivates the discussion of samples, sampling error, sampling biases, etc. 2 Random samples A simple random sample is a subset of the population drawn in such a way that each element of the . | Sampling and estimation Parameters and statistics A parameter is a quantity used to describe a population, and a statistic is a quantity computed from a sample and is used to estimate a population parameter and describe the sample. We typically use statistics to estimate parameters because It isn’t possible to examine the entire population. It isn’t feasible to examine the entire population (., too expensive). In order for a calculated statistic to convey information about the related population parameter, certain conditions must be met; those conditions are generally satisfied if the sample used to calculate the statistics is random. 2 Pages 215–216 Although it is not in the LOS, a reminder of the relationship between statistics and parameters as the basic foundation for sampling processes motivates the discussion of samples, sampling error, sampling biases, etc. 2 Random samples A simple random sample is a subset of the population drawn in such a way that each element of the population has an equal probability of being selected. The key to random sampling lies in the lack of any patterns in the collection of the data elements. Finite and limited populations can be sampled by assigning random numbers to all of the elements in the population, and then selecting the sample elements by using a random number generator and matching the generated numbers to the assigned numbers. If you can enumerate the population, why don’t you just use it? When we can’t identify all the members of the population, we often use kth member sampling, where we select every kth member we observe until we have the necessary sample size. 3 Survey Prediction: Alfred Landon wins over FDR with 57% of the vote to 43% of the vote. – Literary Digest, 1936 LOS: Define simple random sampling. Page 216 One of the most famous examples of a nonrandom sample occurred in the presidential poll predictions in 1936. The Literary Digest, a famous magazine of the time, predicted a victory for Alfred Landon
