#### Univariate k-Means Clustering with elbow method

###### by Jan Freyberg

clusteringIdentify how many clusters your one-dimensional data can be grouped in and how much variance you can explain with these clusters by using the "elbow method".

#### BIC approximation for ANOVA designs

###### by Christoph Huber-Huber

BayesObtain a Bayesian interpretation of your ANOVA results with this app. You just need to enter your sum of squares and some information about your design.

#### N per discovery

###### by Etienne LeBel

paperApp to explore the cost-effectiveness of different research approaches to unearth true scientific discoveries.

#### 2D Outlier analysis

###### by Rajiv Shah

outlierThe app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

#### When does a significant p-value indicate a true effect?

###### by Michael Zehetleitner and Felix Schönbrodt

teachingUnderstanding the Positive Predictive Value (PPV) of a p-value.

#### p-checker: The one-for-all p-value analyzer

###### by Felix Schönbrodt

p-valuesThis Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.

#### What does a Bayes factor look like? (2) Height difference between males and females.

###### by Felix Schönbrodt

BayesteachingCan 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? (1) The urn model.

###### by Felix Schönbrodt

BayesteachingHow much is a BF of 3.7? Well, it is "moderate evidence" for an effect - whatever that means. Let's approach the topic a bit more experientially. What does such a BF look like, visually? We take the good old urn model as an example.

#### A First Lesson in Bayesian Inference

###### by Eric-Jan Wagenmakers

BayesThis app highlights several key Bayesian concepts.

#### A Compendium of Clean Graphs in R

###### by Eric-Jan Wagenmakers

BayesplotWhen done right, graphs can be appealing, informative, and of considerable value to an academic article. This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console.

#### Bayes factor robustness

###### by Felix Schönbrodt

BayesplotCheck how your Bayes factor conclusion depends on the r-scale parameter.

#### Polynomial Surface Explorer

###### by Felix Schönbrodt

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

#### Find-a-fit!

###### by Felix Schönbrodt

regressionplotgameBe an optimizer: Find the best linear fit!

#### Robustness of Mean and Median

###### by Tobias Kächele

quizWhich is more robust against outliers: Mean or median?

#### Normal distribution vs. binomial

###### by Tobias Kächele

quizdistributionApproximating a normal distribution with a binomial distribution

#### Production errors

###### by Tobias Kächele

quizhistogramdistributionGet acquainted with histograms.