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Coke vs. Pepsi Taste Test: Experiments and Inference about Cause part of Examples
The Coke vs. Pepsi Taste Test Challenge has students design and carry out an experiment to determine whether or not students are able to correctly identify two brands of cola in a blind taste test. In the first ...
Reese's Pieces Activity: Sampling from a Population part of Examples
This activity uses simulation to help students understand sampling variability and reason about whether a particular samples result is unusual, given a particular hypothesis. By using first candies, then a web applet, and varying sample size, students learn that larger samples give more stable and better estimates of a population parameter and develop an appreciation for factors affecting sampling variability.
Simulating Size and Power Using a 10-Sided Die part of Examples
This group activity illustrates the concepts of size and power of a test through simulation. Students simulate binomial data by repeatedly rolling a ten-sided die, and they use their simulated data to estimate the size of a binomial test.
Simulating the Effect of Sample Size on the Sampling Distribution of the Mean part of Examples
A java applet that simulates the sampling distribution of the mean. It allows students to explore the effect of sample size.
Using an Applet to Demonstrate Confidence Intervals part of Examples
Students will utilize an applet to further expand their knowledge of confidence intervals.
Influence of Outliers on Correlation part of Examples
In this visualization activity, students will observe their instructor create a scatterplot and observe how the correlation coefficient changes when outlier points are added. Students are then given a follow up assignment which guides them through the applet. In addition, the assignment provides insight about outliers and their effect on correlation.
Simulating a P-value for Testing a Correlation with Fathom part of Examples
This activity has students use Fathom to test the correlation between attendance and ballpark capacity of major league baseball teams by taking a sample of actual data and scrambling one of the variables to see how the correlation behaves when the variables are not related. After displaying the distribution of correlations for many simulated samples, students find an approximate p-value based on the number of simulations that exceed the actual correlation.