### Unit 4: Sampling and Sampling Distributions

While you may not become a professional data gatherer, it is likely that you will need to compile data on a regular basis. When gathering data, you will not always have the luxury of collecting all available data. For example, economists cannot measure the entire unemployment of the population, so they must take a random sample instead. Likewise, in a manufacturing facility, quality control managers do not have the resources to test every product that comes off the line; it is simply not feasible. Instead, they take samples at various points during the production process to test the quality of the products the firm produces.

There are a number of methods employed in sampling data. It is important that the sampling method fits the application. For example, marketing managers may wish to test a product on various groups of people. They may define these groups by age, race, geography, income, or any other factors. They then divide the population into these groups and take samples from each group in a process known as cluster sampling. If marketers do not properly divide the population, they may end up marketing to the wrong demographic and achieving poor sales.

**Completing this unit should take you approximately 3 hours.**

Upon successful completion of this unit, you will be able to:

- differentiate the population from a sample;
- define and apply simple random sampling;
- determine different types of selection bias and sampling errors, and explain how to avoid these errors in survey sampling, such as selection and estimation errors;
- describe and identify the different sampling methods, including systematic, stratified random, cluster, convenience, panel, and quota sampling, and identify an example of each; and
- use a point estimator from a sample to estimate the entire population.

- differentiate the population from a sample;

### 4.1: Sampling and Sampling Distributions

Watch these videos, which explain the nature of sampling distribution and how it changes as sample size changes.

Read this section, which explains sampling in statistics and discusses some of the possible biases when collecting data for a sample. It also explains what to consider when dealing with continuous versus binary random variables.

Watch the first lecture from 1:19:00 to the end, in which Professor Stark covers some problems related to sampling and sampling distributions. Then, watch the second lecture to see some additional examples.

### Unit 4 Problem Set and Assessment

Choose 15 of these problems to solve. To see a solution, click "Show Solution" beneath the problem.

Take this assessment to see how well you understood this unit.

- This assessment
**does not count towards your grade**. It is just for practice! - You will see the correct answers when you submit your answers. Use this to help you study for the final exam!
- You can take this assessment as many times as you want, whenever you want.

- This assessment