A cartoon that can be used to discuss the multiple testing issue and the concept of p-hacking. The cartoon was used in the June 2021 CAUSE cartoon caption contest and the winning caption was written by Jim Alloway from EMSQ Associates. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon that can be used to discuss the importance of using a paired analysis to reduce the variability in the response for a heterogeneous population. The cartoon was used in the February 2021 CAUSE cartoon caption contest and the winning caption was written by Jeremy Case from Taylor University.. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon providing a nice way to introduce the value of data mining for finding patterns in data but not as a gold standard for inference. The cartoon was used in the July 2020 CAUSE cartoon caption contest and the winning caption was written by Charles Eugene Smith from North Carolina State University. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon that provides a clever way to introduce neural networks and machine learning topics. The cartoon was used in the June 2020 CAUSE cartoon caption contest and the winning caption was written by Luis Rivera-Galicia from Alcala University in Spain. The cartoon was drawn by British cartoonist John Landers (www.landers.co.uk) based on an idea by Dennis Pearl from Penn State University.
A cartoon for discussing how subgroup analyses often lead to false positive results (using the comical idea of someone studying your study by having both treatment arms give a placebo). The cartoon is #2726 in the web comic XKCD created by Randall Munroe.
A cartoon for describing both issues associate with meta analyses and with the large number of unreplicated scientific studies. The cartoon is #2755 in the web comic at XKCD.com by Randall Monroe (see https://xkcd.com/2755/).
A cartoon to help discuss both the value and limits of making predictions with large amounts of data. The cartoon was drawn by American cartoonist Jon Carter in 2015.
A humorous cartoon to initiate a conversation about censored data situations such as those seen with survival data. The cartoon is drawn by American cartoonist Jon Carter in 2015.
A song designed to assist in teaching the basics of Multi-Armed Bandits, which is a type of machine learning algorithm and is the foundation for many recommender systems. These algorithms spend some part of the time exploiting choices (arms) that they know are good while exploring new choices. The song (music and lyrics) was written in 2021 by Cynthia Rudin from Duke University and was part of a set of three data science oriented songs that won the grand prize in the 2023 A-mu-sing competition. The lyrics are full of double entendres so that the whole song has another meaning where the bandit could be someone who just takes advantage of other people! The composer mentions these examples of lines with important meanings:
"explore/exploit" - the fundamental topic in MAB!
"No regrets" - the job of the bandit is to minimize the regret throughout the game for choosing a suboptimal arm
"I keep score" - I keep track of the regrets for all the turns in the game
"without thinking too hard," - MAB algorithms typically don't require much computation
"no context, there to use," - This particular bandit isn't a contextual bandit, it doesn't have feature vectors
"uncertainty drove this ride." - rewards are probabilistic
"I always win my game" - asymptotically the bandit always finds the best arm
"help you, decide without the AB testing you might do" - Bandits are an alternative to massive AB testing of all pairs of arms
"Never, keeping anyone, always looking around and around" - There's always some probability of exploration throughout the play of the bandit algorithm
A music video designed to assist in teaching the basics of Multi-Armed Bandits, which is a type of machine learning algorithm and is the foundation for many recommender systems. These algorithms spend some part of the time exploiting choices (arms) that they know are good while exploring new choices (think of an ad company choosing an advertisement they know is good, versus exploring how good a new advertisement is). The music and lyrics were written by Cynthia Rudin of Duke University and was one of three data Science songs that won the grand prize and first in the song category for the 2023 A-mu-sing competition.
The lyrics are full of double entendres so that the whole song has another meaning where the bandit could be someone who just takes advantage of other people! The author provides these examples of some lines with important meanings:
"explore/exploit" - the fundamental topic in MAB!
"No regrets" - the job of the bandit is to minimize the regret throughout the game for choosing a suboptimal arm
"I keep score" - I keep track of the regrets for all the turns in the game
"without thinking too hard," - MAB algorithms typically don't require much computation
"no context, there to use," - This particular bandit isn't a contextual bandit, it doesn't have feature vectors
"uncertainty drove this ride." - rewards are probabilistic
"I always win my game" - asymptotically the bandit always finds the best arm
"help you, decide without the AB testing you might do" - Bandits are an alternative to massive AB testing of all pairs of arms
"Never, keeping anyone, always looking around and around" - There's always some probability of exploration throughout the play of the bandit algorithm