This Java Applet lets you explore various aspects of sampling distributions.

Presents a problem that is similar to what might be on an AP Statistics exam. Walks students through each step for solving it and students input answers to questions along the way.

Students study the distribution of sample proportions with a simulation. They find a small sample size leads to skewed distribution and as the sample size increases, the distribution becomes more normal.

This activity covers the distribution of a difference between two independent sample proportions or two independent sample means.

This learning module discusses quantitative and qualitative data, the use of two-way tables in marginal and conditional distributions, how to display data, variation in data and samples, and how to critically evaluate statistical...

[Free Registration/Login may be required to access all resource tools.] This concept introduces students to the standard normal probability distribution.

This site provides an overview of sampling distribution and associated terms.

Students use Reese's Pieces to investigate the proportion of orange candies present in a set of 25. After collecting data for the class, they use a web applet to collect more data for random samples. This is compared to the actual...

The different types of distributions are discussed here - uniform, regular, negative exponential, triangular, and normal. The properties of probability distributions are also explained here in detail. A good starting point in the topic...

This discussion of statistical t distributions is presented in notes with examples added as needed.

Students learn about exponential, normal, and beta distributions. The tutorial investigates the statistics concepts through class notes and examples. Review exercises and a normal probability calculator are included.

This section explores the concept of sampling variability in the sample proportion. It will address the effect on the mean and standard deviation of a sample distribution as the sampling size changes.

In this detailed lesson looking at text messaging behavior, students investigate the sampling distribution for the sample mean to learn what factors affect its shape. They will also be introduced to the Central Limit Theorem.

A learning activity where learners are guided into discovering the Central Limit Theorem. As they explore sampling distributions, they will identify predictable patterns which can be described with this mathematical theorem. Handouts for...

[Free Registration/Login may be required to access all resource tools.] Understanding survey data and methods of sampling.

[Free Registration/Login may be required to access all resource tools.] This Concept introduces students to applications of the Normal Distribution.

[Free Registration/Login may be required to access all resource tools.] Covers probability distributions where there are more than two outcomes for an experiment.

[Free Registration/Login may be required to access all resource tools.] Covers exponential distributions. Presents examples using a graphing calculator as well as guided and independent practice questions.

[Free Registration/Login may be required to access all resource tools.] This Concept introduces the Student's t-distribution and degrees of freedom.

Given a problem situation, the student will evaluate a method of sampling to determine the validity of an inference made from the set of data.

Given a context, create a probability distribution. Students receive immediate feedback and have the opportunity to try questions repeatedly, watch a video or receive hints.

This lesson explains how to calculate and interpret the confidence interval for a population mean. It also looks at the effect on the confidence interval of changing the confidence level or sample size. It then explains how to find the...

This site from Cornell University contains great information on random or probability sampling. Definitely a site worth checking out on the subject.

Students perform single and two-sample t procedures to investigate special cases of normally distributed data. They explore inference for a least-square regression line, including the confidence interval.