Sampling from a Normal Distribution(0)
This web visualization demonstrates the concept of a sampling distribution of an estimate, using the example of a mean of a Normally distributed variable. It also reinforces the idea of a histogram.
- Familiarity with methods of summarizing data sets, such as mean and standard deviation
- The ability to recognize probability models as distributions with shape, centre, and spread
- The ability to recall the key properties of the Normal model
- Identify and distinguish between a population and a sample, and between parameters and statistics
- Interpret histograms for summarizing and comparing data sets
- Explain the concepts of sampling variability and sampling distribution
- Describe properties of the sampling distribution of the sample mean
- Explain whether and how the population distribution and the sample size influence the sampling distribution of the sample mean from a Normal distribution
University of British Columbia
Boris Dalstein, Mike Whitlock & Zahraa Almasslawi
Melissa Lee, Gaitri Yapa & Bruce Dunham
Darren Irwin, Dolph Schluter, Nancy Heckman, Kaylee Byers, Brandon Doty, Kim Gilbert, Sally Otto, Wilson Whitlock, Jeff Whitlock, Jeremy Draghi, Karon MacLean, Fred Cutler, Diana Whistler, Andrew Owen, Mike Marin, Leslie Burkholder, Eugenia Yu, Doug Bonn, Michael Scott, the UBC Physics Learning Group & the UBC Flex Stats initiative for numerous suggestions and improvements.
Suggested Uses, Tips and Discoveries
- Whitlock, Michael