Interactive apps are a powerful way to engage students with statistical concepts. Several textbooks have apps associated with them, for instance, the Agresti/Franklin/Klingenberg book, Statistics: The art and science of learning from data, the Tintle et al book, Introduction to Statistical Investigations, the Lock^5 book Statistics: Unlocking the power of data. Even though these apps are associated with textbooks, many are free to use without the book. What more could an instructor ask?
As part of the StatPREP project, we have also been developing apps. The StatPREP philosophy is to be data front-and-center in statistics. Consequently, our apps are based entirely in displays of data. And each app uses a standard graphical modality for displaying data. We do add annotations – in particular, confidence intervals – and many of the apps display resampling in the same space as the original data.
You can try them out by following these links. (We recommend that you open the links one at a time in a new browser tab. There’s no point in opening them all at once.)
Note for those using the original alpha release. We are gradually re-writing the apps to include better documentation and to display better on small screens such as mobile phones.
- Center and spread app and instructor orientation
- Displaying distributions app and instructor orientation
- Stem-and-leaf app and instructor orientation
- Why density instead of counts? app and instructor orientation. (Previous alpha version of the app.
- Resampling and confidence app and instructor orientation
- Proportions app and instructor orientation
- Two-sample t test app and instructor orientation
- ANOVA in one variable app and instructor orientation
- Smoothing and covariates app and instructor orientation
The design of these apps follows principles consistent with the goals of StatPREP.
- There’s always real data behind the apps.
- The displays are always genuine modes of displaying data.
- Most of the apps introduce the possibility of exploring the data and finding out something about the world.
- We try hard to avoid purely theoretical constructions. For instance, we use simulation to generate samples and resamples.
- We de-emphasize p-values in accordance with the recommendations of the American Statistical Association.
- Following the GAISE report, we emphasize multi-variate thinking. There’s almost always a response and explanatory variable. (As we introduce new apps about modeling, there will be covariates as well.)
- Statistics are always displayed in the context of the whole range of data. For instance, if there’s a mean and its confidence interval, that’s plotted on top of the case-by-case data so that students see clearly the variation in the data.
- There’s usually a way to see how results can depend on sample size.
- The apps are unadorned. An instructor who wants to embed an app in a lesson worksheet or an interactive tutorial can do so.