When an experimenter is interested in the effects of two or more independent variables, it is usually more efficient to manipulate these variables in one experiment than to run a separate experiment for each variable. Moreover, only in experiments with more than one independent variable is it possible to test for interactions among variables. Experimental designs in which every level of every variable is paired with every level of every other variable are called factorial designs.
Within-subject designs are designs in which one or more of the independent variables are within-subject variables. Within-subjects designs are often called repeated-measures designs since within-subjects variables always involve taking repeated measurements from each subject. Within-subject designs are extremely common in psychological and biomedical research.
When two variables are related, it is possible to predict a person's score on one variable from their score on the second variable with better than chance accuracy. This section describes how these predictions are made and what can be learned about the relationship between the variables by developing a prediction equation.
This chapter discusses a collection of tests called distribution-free tests, or nonparametric tests, that do not make any assumptions about the distribution from which the numbers were sampled. The main advantage of distribution-free tests is that they provide more power than traditional tests when the samples are from highly-skewed distributions.
Measures of the size of an effect based on the degree of overlap between groups usually involve calculating the proportion of the variance that can be explained by differences between groups. This resource outlines different approaches to measuring this proportion.
Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.
A collection of Java applets and simulations covering a range of topics (descriptive statistics, confidence intervals, regression, effect size, ANOVA, etc.).
Gapminder seeks to educate all on the importance of "factfulness" and of knowing and contextualizing the statistics that describe the state of our world. Learn facts from across the globe such as average income, life expectancy, energy use, education levels, and much more.
Download Gapminder’s slides, tools, posters, handouts, lesson plans, and presentations at this webpage.
Gapminder seeks to educate all on the importance of "factfulness" and of knowing and contextualizing the statistics that describe the state of our world. Learn facts from across the globe such as average income, life expectancy, energy use, education levels, and much more.
This particular page gives teachers resources to use in their classrooms involving the tools and data found on Gapminder.
This is the free online textbook for the Foundations of Data Science class at UC Berkeley for the Data 8 Project. Creators have used https://github.com/data-8/textbook to maintain this textbook (an open source project that allows for continual easy editing and maintenance).