Resource Library

A cartoon to be used for discussing the importance of efficiency in sampling. The cartoon was used in the April 2017 CAUSE Cartoon Caption Contest. The winning caption was submitted by Mickey Dunlap from University of Georgia. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. Three honorable mentions that rose to the top of the judging in the April competition included "Better to ask for help BEFORE you're drowning in data!," written by Larry Lesser from University of Texas at El Paso; "I guess I should have asked for more details before signing up for this "Streaming Data" workshop," written by Chris Lacke from Rowan University; and "On reflection, random sampling WITH replacement might not have been appropriate in this scenario," written by Aaron Profitt from God's Bible School and College.

A joke to help in discussing Latin Square experimental designs. The joke was written by Larry Lesser from The University of Texas at El Paso in November, 2018.

A cartoon to be used for in discussing human subjects issues during a unit on designing experiments. The cartoon was used in the August 2018 CAUSE Cartoon Caption Contest. This winning caption was submitted by Greg Snow from Brigham Young University. The drawing was created by British cartoonist John Landers based on an idea from Dennis Pearl of Penn State University. An honorable mention for a caption that also rose to the top of the judging in the August competition was "The Poisson model for rare events was about to be tested," which may be found at https://www.causeweb.org/cause/resources/fun/cartoons/twins-ii

A joke to aid in discussing Confirmation Bias (bias introduced in surveys because respondents tend to interpret things in a way that confirms their preexisting beliefs).  The joke was written by Larry Lesser from The Universisty of Texas at El Paso and Dennis Pearl from The Pennsylvania State University in October, 2018.

A joke to help in discussing Latin Square experimental designs. The joke was written by Larry Lesser from The University of Texas at El Paso in October, 2018.

Explore the Hubble Deep Fields from a statistical point of view.  Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).

This resource provides information on all of the astronauts that have been a part of the U.S. space program (as well as some facts about other countries' space programs), including how many flights each has participated in, where they are from, where they attended college, and many more fun facts.  This material contacts a great deal of data on these individuals and could be used as data sets for teaching basic statistics concepts.

The purpose of this work is to provide a comprehensive reference for facts about Project Apollo, America’s effort to put humans on the Moon.  While there have been many studies recounting the history of Apollo, this new book in the NASA History Series seeks to draw out the statistical information about each of the flights that have been long buried in numerous technical memoranda and historical studies. It seeks to recount the missions, measuring results against the expectations for them.

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA’s objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. This PRA Procedures Guide, in the present second edition, is neither a textbook nor an exhaustive sourcebook of PRA methods and techniques. It provides a set of recommended procedures, based on the experience of the authors, that are applicable to different levels and types of PRA that are performed for aerospace applications. 

This NASA-HANDBOOK is published by the National Aeronautics and Space Administration (NASA) to provide a Bayesian foundation for framing probabilistic problems and performing inference on these problems. It is aimed at scientists and engineers and provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. The overall approach taken in this document is to give both a broad perspective on data analysis issues and a narrow focus on the methods required to implement a comprehensive database repository.