Sampling distributions show the distribution of a statistic from all possible samples and are the cornerstones of inferential statistics. Thus, understanding sampling distributions, the subject of this module, is key as we transition to performing inferential statistics.
After completing this module, you should be able to ...
- Describe the concept of sampling variability.
- Describe why sampling variability must be dealt with to make inferences.
- Describe what a sampling distribution represents.
- Identify how a sampling distribution differs from a population distribution.
- Describe what a standard error is.
- Identify how a standard error differs from a standard deviation.
- Describe how and why sampling distributions are simulated.
- Explain the concepts of precision, accuracy, and bias as it relates to statistics and parameters.
- Describe the theoretical distribution of the sampling distribution of the sample means.
- Gain some belief that the theoretical distribution actually represents the sampling distribution of the sample means.
- Use the sampling distribution of sample means to compute the probability of particular sets of means.
Preparation for Class
You should read this and watch these videos:
- Introduction to Sampling Distributions – A [9 mins] and B [8 mins]
- Central Limit Theorem [4 mins]
- Types of Questions [6 mins]