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?
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?
Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.
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.
Plot the theoretical p-value distribution and power curve for an independent t-test based on the effect size, sample size, and alpha.
Explore the Vovk-Sellke Maximum p-Ratio, a measure that indicates the maximum diagnosticity of a given p-value. Choose your own p-value to find out how diagnostic it is for your research!
This issue contains articles about Karl Pearson (150 years after his birth); finding more ways to make learning statistics fun; simulating capture-recapture sampling in Excel and by hand; common misconceptions in statistics; a correlation-based puzzler and a STAT.DOKU puzzle.
This hour long radio podcast focuses on stochasticity, or randomness. According the website: "Stochasticity (a wonderfully slippery and smarty-pants word for randomness), may be at the very foundation of our lives. To understand how big a role it plays, we look at chance and patterns in sports, lottery tickets, and even the cells in our own body. Along the way, we talk to a woman suddenly consumed by a frenzied gambling addiction, meet two friends whose meeting seems to defy pure chance, and take a close look at some very noisy bacteria." Several guests appear in this radio podcast, including Deborah Nolan.
A song for use in helping students to apply relationships among alpha, p-value, and the decision of a hypothesis test. Music & Lyrics © 2015 by Lawrence M. Lesser from The University of Texas at El Paso. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
Statistics and probability concepts are included in K–12 curriculum standards—particularly the Common Core State Standards—and on state and national exams. STEW provides free peer-reviewed teaching materials in a standard format for K–12 math and science teachers who teach statistics concepts in their classrooms.
STEW lesson plans identify both the statistical concepts being developed and the age range appropriate for their use. The statistical concepts follow the recommendations of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report: A Pre-K-12 Curriculum Framework, Common Core State Standards for Mathematics, and NCTM Principles and Standards for School Mathematics. The lessons are organized around the statistical problemsolving process in the GAISE guidelines: formulate a statistical question, design and implement a plan to collect data, analyze the data by measures and graphs, and interpret the data in the context of the original question. Teachers can navigate the STEW lessons by grade level and statistical topic.
The Journal of Statistics Education provides a collection of Java applets and excel spreadsheets (and the articles associated with them) from as early as 1998 on this webpage.