This applet lets you explore the effect of violations of the assumptions of normality and homogeneity of variance on the type I error rate and power of t tests (and two-group analysis of variance).
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 applet demonstrates how a histogram is affected by bin width and starting point of first bin. It also illustrates cross-validation criterion for assessing histograms.
In this demonstration a scatterplot is displayed and you draw in a regression line by hand. You can then compare your line to the best least squares fit. You can also try to guess the value of Pearson's correlation coefficient.
As described on the page itself: "The simulation shows a scatterplot of data from a bivariate distribution in which the relationship between the two variables is linear. You can change the "input" values of slope, standard error of the estimate, or standard deviation of X for this data sample, and see the effects of your change. "
This is a free online video program. "How to recognize a two-sample problem and how to distinguish such problems from one- and paired-sample situations are the subject of this program. A confidence interval is given for the difference between two means, using the two-sample t statistic with conservative degrees of freedom."
This free online video program "lays out the parts of the confidence interval and gives an example of how it is used to measure the accuracy of long-term mean blood pressure. An example from politics and population surveys shows how margin of error and confidence levels are interpreted. The program also explains the use of a formula to convert the z* values into values on the sampling distribution curve. Finally, the concepts are applied to an issue of animal ethics."
This free online video program "shows how to improve the accuracy of a survey by using stratified random sampling and how to avoid sampling errors such as bias. While surveys are becoming increasingly important tools in shaping public policy, a 1936 Gallup poll provides a striking illustration of the perils of undercoverage."
