# Multimedia

• ### Normal v. Binomial Distribution (Shiny App)

Approximating a normal distribution with a binomial distribution

• ### A First Lesson in Bayesian Inference (uses Shiny Apps)

This page supports an in-class exercise that highlights several key Bayesian concepts. The scenario is as follows: a large paper bag contains pieces of candy with wrappings of different color, and we are interested in learning about the unknown proportion of yellow-wrapped pieces of candy. After completing the exercises, we will be familiar with the following concepts and ideas: probability distributions can quantify degree of beliefprior distributionposterior distributionsequential updatingconjugacy, Cromwell’s Rule (http://en.wikipedia.org/wiki/Cromwell's_rule), the data overwhelm the prior, Bayes factors, Savage-Dickey density ratio, sensitivity analysiscoherence.

• ### Find-a-fit! (Shiny App)

Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

• ### Polynomial Surface Explorer (Shiny App)

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

• ### When does a significant p-value indicate a true effect? (Shiny App)

When does a significant p-value indicate a true effect?  This app will help with understanding the Positive Predictive Value (PPV) of a p-value.

This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124

• ### Feeling Bayes Factor: Height Difference Between Males and Females (Shiny App)

Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

• ### What does a Bayes factor look like? [The urn model] (Shiny App)

Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.

• ### True Scientific Discoveries: What Research Approach is Most Cost-Effective? (Shiny App)

Use presets or change parameter values manually to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries. For detailed explanation and conceptual background, see LeBel, Campbell, & Loving (in press, JPSP), Table 3. This app is an extension of Zehetleitner and Felix Schönbrodt's (2016) positive predictive value app

• ### Practicing Statistics: Guided Investigations for the Second Course

The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:

• An introductory case study focusing on a particular statistical method in order to encourage students to experience data analysis as it is actually practiced.
• guided research project that walks students through the entire process of data analysis, reinforcing statistical thinking and conceptual understanding.
• Optional extended activities that provide more in-depth coverage in diverse contexts and theoretical backgrounds. These sections are particularly useful for more advanced courses that discuss the material in more detail. Some Advanced Lab sections that require a stronger background in mathematics are clearly marked throughout the text.
• Data sets from multiple disciplines and software instructions for Minitab and R.

The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class.

• ### Understanding Global Terrorism

The Global Terrorism Database (GTD) contains information about more than 140,000 terrorist incidents occurring between 1970 and 2014. The data in the GTD are gathered from information gathered through multiple news sources (LaFree, Dugan, & Miller, 2015). In this activity, we will study the extent to which chemical, biological, radiological, and nuclear (CBRN) weapons have been used so far. We analyze whether or not their past use fits with our perceptions. Have CBRN weapons been used successfully in the past? Which weapons are more historically dangerous (more fatalities, injuries) in the hands of terrorists? What are the implications of past usage of CBRN weapons compared to other weapons in determining our priorities in counter-terrorism policies?