# Getting started with the "New" Statistics # This is the OSF page for a workshop on getting started with the "New Statistics" that will be given 12:30pm Pacific Time on June 22, 2020 during the SIPS 2020 virtual meeting. The workshop is organized by Bob Calin-Jageman with assistance from moderators TJ Krafnick, Persis Driver, and Geoff Cumming. Here you will find: - [The slides from the workshop][1] - [The sample data for the workshop][2] - [Instructions on how obtain esci for jamovi][3], the main tool we'll use for estimating parameters for different research designs - [Instructions on how to obtain and use the esci package for R][4], a still-in-development package for obtaining estimates for different research designs - [Links to sample data that you can use to teach the estimation approach][5] - (eventually) A video of the workshop. ## Resources for going forward ## ### Teaching the New Stats ### * Videos to use with a flipped classroom: https://www.youtube.com/user/geoffdcumming * Datasets from multi-lab psych studies: https://github.com/rcalinjageman/MultiLab_Datasets_For_Teaching * Touch base with Bob for a complete set of instructor materials * Share materials back to the community via this OSF page: https://osf.io/muy6u/ ### Some Suggested Readings ### * Frequentist Estimation: Cumming, G., & Calin-Jageman, R. J. (2017). Introduction to the new statistics: Estimation, open science, and beyond. New York: Routledge. Request a free desk copy here: https://www.routledge.com/textbooks/evaluation/9781138825529 * Bootstrap Estimation: Hesterberg, T. C. (2015). What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum. American Statistician, 69(4), 371–386. https://doi.org/10.1080/00031305.2015.1089789 * Bayesian Estimation - Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178–206. https://doi.org/10.3758/s13423-016-1221-4 * When testing, do better with inference by interval - Lakens, D. (2017). Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Social Psychological and Personality Science, 8(4), 355–362. https://doi.org/10.1177/1948550617697177 * Planning for Precision - Rothman, K. J., & Greenland, S. (2018). Planning Study Size Based on Precision Rather Than Power. Epidemiology, 29(5), 599–603. https://doi.org/10.1097/EDE.0000000000000876 * Planning for evidence - Schönbrodt, F. D., & Wagenmakers, E.-J. (2017). Bayes factor design analysis: Planning for compelling evidence. Psychonomic Bulletin & Review, 1–16. https://doi.org/10.3758/s13423-017-1230-y ### Software ### Frequentists estimation (confidence intervals) * esci module for jamovi - https://osf.io/d89xg/wiki/tools:%20esci%20for%20jamovi/ * Guide to esci package for R - https://osf.io/d89xg/wiki/tools:%20esci%20for%20R/ * Esci for jamovi… still in progress Boostrapped intervals * DABEST for R, python, and web-interface - http://www.estimationstats.com/ Bayesian Estimation (credible intervals) * JASP - https://jasp-stats.org/ * BEST package for R - https://cran.r-project.org/web/packages/BEST/ [1]: https://osf.io/at8kg/download [2]: https://osf.io/wdq8m/ [3]: https://osf.io/d89xg/wiki/tools:%20esci%20for%20jamovi/ [4]: https://osf.io/d89xg/wiki/tools:%20esci%20for%20R/ [5]: https://osf.io/d89xg/wiki/data:%20data%20from%20multi-lab%20psych%20studies%20for%20teaching%20statistics/