A joke to use in discussing the poor representativeness of a convenience sample. The joke was written in 2019 by Larry Lesser from The University of Texas at El Paso.
A joke to use in discussing the poor representativeness of a convenience sample. The joke was written in 2019 by Larry Lesser from The University of Texas at El Paso.
A cartoon that can be a vehicle to discuss the nature of convenience samples and how they are likely to differ from probability-based samples. The cartoon was used in the January, 2018 CAUSE cartoon caption contest and the winning caption was submitted by Larry Lesser from The University of Texas at El Paso. 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 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 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.
This page calculates the standard error of a sampling distribution of sample means when users input the mean and standard deviation of the population and the sample size.
Illustrates the central limit theorem by allowing the user to increase the number of samples in increments of 100, 1000, or 10000.
This page generates a graph of the Chi-Square distribution and displays the associated probabilities. Users enter the degrees of freedom (between 1 and 20, inclusive) upon opening the page.
An application of Bayes Theorem that performs the same calculations for the situation where the several probabilities are constructed as indices of subjective confidence.
This page will generate a graphic and numerical display of the properties of a binomial sampling distribution, for any values of p and q, and for values of n between 1 and 40, inclusive.
The page will calculate the following: Exact binomial probabilities, Approximation via the normal distribution, Approximation via the Poisson Distribution. This page will calculate and/or estimate binomial probabilities for situations of the general "k out of n" type, where k is the number of times a binomial outcome is observed or stipulated to occur, p is the probability that the outcome will occur on any particular occasion, q is the complementary probability (1-p) that the outcome will not occur on any particular occasion, and n is the number of occasions.