This simulation shows recorded response times on a simple motor task under two conditions. Various statistics and graphs used to compare the distributions are presented.
This case study addresses the question: "Will a smiling person accused of a crime be treated more leniently than one who is not smiling? If so, does the type of smile make a difference?" It concerns the following concepts: quantile/boxplots, contrasts among means, Dunnett's test, and Bonferroni correction.
This interactive tutorial on Basic Probability helps students understand the basic concepts of probability, define independent and compound events, use the basic properties of probability, understand the concept of conditional probability, and solve exercise problems using basic probability.
This tutorial on Random Variables helps students understand the definition of random variables, recognize and use discrete random variables, recognize and use continuous random variables, and solve exercise problems using random variables.
This interactive tutorial on Expectations helps students understand the concept of expectations, recognize and use variance and standard deviation, understand the method of moments, recognize and use co-variance, and solve exercise problems using expectations.
This tutorial on Distributions helps students understand the basic concept of probability distributions, recognize and use Binomial, Normal, Poisson, and Uniform Distributions, and solve exercise problems using probability distributions.
This self-test provides a review/assessment of the Probability section of this module. At the bottom, there is a grading button to rate the users' understanding of the material.
This module is a short quiz which gives a review/assessment of the main concepts for this refresher course. At the bottom, there is a grading button to rate the understanding of the material.
In this free online video program, "students will learn the distinction between deterministic phenomena and random sampling. This program introduces the concepts of sample space, events, and outcomes, and demonstrates how to use them to create a probability model. A discussion of statistician Persi Diaconis's work with probability theory covers many of the central ideas about randomness and probability."
