Resource Library

This dataset comes from a study on drug treatments of reflux disease patients. Twelve patients were assigned to a four period crossover design, and data on their disease symptoms were collected after treatment. Questions this study focused on refer to dosage of the drug. A text file version of the data is found in the relation link.

This resource explains margin of error using an example on presidential popularity polls.

This dataset comes from a study on pregnant rats. Forty rats were given 4 doses of a drug, and data on their fetuses were collected. Questions this study focused on refer to the relationship between dosage of the drug and gender of the fetus. A text file version of the data is found in the relation link.

This module discusses the history and importance of the normal distribution, as well as normal moments, the standard normal distribution, normal probabilities, Z-Scores, and normal quantiles. The applet allows users to compute normal probabilities and quantiles. Three follow-up examples cover cholesterol, male heights, and mean temperatures for various cities in South Carolina.

This applet allows students to explore three methods for measuring "goodness of fit" of a linear model. Users can manipulate both the data and the regression line to see changes in the square error, the absolute error, and the shortest distance from the data point to the regression line.

This lesson poses a series of questions designed to challenge students' possible misconceptions of statistical inference and hypothesis testing. The lesson uses the statistical software, Fathom, and three datasets with information on the number of chips per canister distributed by a snack maker. The data can found at the relation address below.

This page provides a table for selecting an appropriate statistical method based on type of data and what information is desired from the data. It also compares parametric and nonparametric tests, one-sided and two-sided p-values, paired and unpaired tests, Fisher's test and the Chi-square test, and regression and correlation. It comes from Chapter 37 of the textbook, "Intuitive Biostatistics".

This collection of datasets comes from several phases of drug research. Each dataset comes with a full description and questions to answer from the data.

This Flash based applet simulates data from a case study of treatments for tumor growth in mice. This simulation allows the user to place mice into a control and treatment groups. The simulation then compares the difference in the groups based on this haphazard selection to those of a truly random assignment (the user may also create multiple random assignments and examine the sampling distribution of key statistics). The applet may be used to illustrate three points about random assignment in experiments: 1) how it helps to eliminate bias when compared with a haphazard assignment process, 2) how it leads to a consistent pattern of results when repeated, and 3) how it makes the question of statistical significance interesting since differences between groups are either from treatment or by the luck of the draw. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and assessments. Key Note for Instructors: The data are drawn from a real experiment with an effective treatment but where the response is correlated with animal age and size (so tumor size will tend to be smaller in the treatment group when measured at the end of a randomized experiment but animal age and size should not be). Typically people choosing haphazardly will tend to pick larger/older animals for the treatment group and thus create a bias against the treatment.

This article provides the example of student form orders to demonstrate the unreliability of combining data from two different distributions (or subjects).