# Multimedia Resources

Click on the following links. Please note these will open in a new window.

Audio 1: The Challenges of Polling
Description: Touches on sampling issues to describe the challenges of contemporary opinion polling.
LO: 4-1: Compare different methods for creating a sample from a population.

Video 1: A Brief Introduction to Sampling
Description: Descriptions of the types of sampling paired with real research examples.
LO: 4-1: Compare different methods for creating a sample from a population.

Video 2: Populations Versus Samples
Description: This clip describes the difference between population and samples using a cute example of endangered pandas.
LO: 4-1: Compare different methods for creating a sample from a population.

Video 3: Sampling Techniques
Description: This clip describes different sampling techniques.
LO: 4-1: Compare different methods for creating a sample from a population.

Video 4: Sampling Distribution
Description: This clip describes and surveys some of the major statistical tests, as well as underscores the importance of the central limit theorem using a song and cartoon to the tune of “I’m Gonna Be (500 Miles)” by The Proclaimers.
LO: 4-2: Evaluate how well a sample represents a population.

Video 5: Sampling Error in Daily Life
Description: This TED talk explores the implications of sampling from a population for everyday life.
LO: 4-3: Understand how sampling error occurs.

Web 1: Populations and Samples in Everyday Life
Description: An unusual social engineering experiment tried to apply what’s known about peer effects to the real world.
LO: 4-1: Compare different methods for creating a sample from a population.

Web 2: Sampling Distribution Demonstration
Description: This online applet illustrates the sampling distribution of the mean.
LO: 4-2: Evaluate how well a sample represents a population.

Web 3: What Might Make Young People Practice Safe Sex? Lottery Tickets!
Description: An example of recruiting participants to answer an important research question.
LO: 4-3: Understand how sampling error occurs.