At the recent 2019 Canadian Psychological Association convention, I gave a workshop on Cloud-Based Statistics Apps that could potentially be useful for teaching. I’m posting the list of applications here in case anyone finds them useful for teaching. In addition, also posting my slides from the talk, where I describe my approach to using these apps when teaching undergrad statistics.

**Central Limit Theorem (Means)
**

https://gallery.shinyapps.io/CLT_mean/

*This application runs simulations to demonstrate the central limit theorem from different population distributions by sampling up to 1000 times from a population. It shows the population distribution, the first 10 sample distributions, and the sampling distribution. This apps helps enormously with teaching students the central limit theorem. It can sample from normal, uniform, right-skewed, and left-skewed population distributions. *

**Central Limit Theorem (Proportions)
**

https://gallery.shinyapps.io/CLT_prop/

*This app is like the one above, except that it shows the sampling distribution for proportions.
*

**Regression Diagnostics
**

https://gallery.shinyapps.io/slr_diag/

*This app simulates new data for each run and shows students how to review diagnostic plots from linear
regression models. It allows students to simulate situations where the normality assumption is likely to
be met as well as situations where the assumptions are violated (quadratic relationships, non-constant
variance). Helpful for getting students to interpret plots of residuals.
*

**Generic Distribution Calculator
**

https://gallery.shinyapps.io/dist_calc/

*Calculates area under the curve for various distributions, such as z, binomial, t, F and χ2**. **Useful when teaching students about standardizing distributions (e.g., converting to z-scores) and for conceptual understanding of p-values. *

Note that Bruce Dudek does not post his code for these apps online. However, he may be willing to share code with you if you send an email (I use some of these apps in my own classes!).

**Distributions (z, t, F & chi-squared)
**

https://shiny.rit.albany.edu/stat/stdnormal/

*Bruce developed a set of distribution apps that improve upon the generic ShinyEd one above in some
ways. These apps are primarily useful for looking up p-values, and can replace looking up p-values in
conventional critical value tables from old-school statistics textbooks. If the emphasis is entirely on
looking up p-values, these apps work better.
*

**Descriptive Statistics and Visualization
**

https://shiny.rit.albany.edu/stat/describe/

*This is a cool app that has a few datasets built in, and allows students to calculate descriptive statistics
(e.g., means, SDs, range) as well as a few basic data visualizations (e.g., histograms, box plots). Can be
useful as a way to introduce students to exploring data with graphs.
*

**Confidence Intervals
**

https://shiny.rit.albany.edu/stat/confidence/

*This app simulates confidence intervals (up to 100 at a time). It is useful to help students understand the
conceptual issues surrounding confidence intervals, and the impact of sample size and confidence limit
(e.g., 90% vs 95%) changes the width of the intervals.
*

**RShiny Apps from Other Sources **

**ANOVA Playground **

https://rna.wlu.edu/shiny-apps/anovaPlayground/

*This app has a LOT going on. It can allow the user to simulate data and run a one-way ANOVA, complete
with beautiful data visualizations. It can also allow the user to read in data to run a one-way ANOVA on
a pre-specified dataset. Finally, it also allows the user to run individual t-tests as a rudimentary form of
post-hoc test. This app is very useful to help students understand and interpret one-way ANOVA.
*

**Other Online Apps th****at Don’t Use RShiny
**

The applications below don’t actually use RShiny but are similar in the sense that they can be run in a web browser without having to download software. I’ve found them to be useful in teaching.

**Guess the Correlation
**

http://guessthecorrelation.com/

*A game that shows students a scatterplot and asks them to guess the magnitude. Very fun.
*

**DrawMyData
**

http://www.robertgrantstats.co.uk/drawmydata.html

*This allows you to add dots to a scatterplot with a point-and-click format, calculates the correlation
coefficient and allows you to save the resulting dataset. Insanely useful for teaching.
*

**Rock ‘n Poll
**

http://rocknpoll.graphics/index.html

*Explains the concept of sampling variability using political polling as an example. Very beautiful viz.
*

**Rossman/Chance Java Applet Collection
**

http://www.rossmanchance.com/applets/

*This site has been around a long time and uses java and javascript. Many different simulations, some
that dup**licate those above, some new. They are not as “pretty” as some of the other options, but they
**are still very useful, and notably have some simulations on probability. Note: The older ones that use java
**don’t seem to run on all computers; the javascript ones (labeled “js”) seem to work fine.
*

**Kristoffer Magnusson’s d3.js Visualizations
**

https://rpsychologist.com/d3/NHST/

https://rpsychologist.com/d3/CI/

https://rpsychologist.com/d3/cohend/

https://rpsychologist.com/d3/tdist/

https://rpsychologist.com/d3/equivalence/

*Kristoffer has made some truly excellent data visualizations worth checking out. The ones above cover
null hypothesis significance testing, confidence intervals, effect size, t-distributions, and equivalence
**testing. I honestly can’t hype these enough, they are all top**-notch.
*

**Online cloud-based statistics software
**

https://www.graphpad.com/quickcalcs/

http://powerandsamplesize.com/

https://www.socscistatistics.com/tests/

*Sometimes, you want your students to analyze some data in a similar fashion to how they would if they
had conventional software installed (e.g., SPSS, SAS). All of these do all of the calculations online, and
remove the need for students to install software **– **which, I have discovered, is a very error-prone process
for many students. Useful if you want students to do statistical calculations in a class where learning a
particular software is not the goal.
*

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