Get Started

Here are some handy resources to get you started with the New Statistics


  • Of course, we think Introduction to the New Statistics is a great source for learning the New Statistics approach.  But there are lots of other really good resources out there.
  • To learn the Bayesian approach to the New Statistics (which we heartily recommend):
  • Learn about estimation from one of the masters:
    • Statistics with Confidence (Smithson, 2000)
    • Confidence Intervals (Smithson, 2003)
  • And here are some helpful papers on understanding effect sizes and/or meta-analysis:
    • A great primer on effect sizes, the subtle differences between effect sizes for different designs, etc:  (Lakens, 2013)
    • An excellent tutorial on effect sizes and how to report them (Pek & Flora, 2017)
    • The bible of effect sizes and meta-analysis: (Cooper, Hedges, & Valentine, 2009)


  • ESCI is a free set of Excel spreadsheets that can help provide New Statistics analysis and figures for many common research designs.  It is available for download directly from this website: ESCI
  • ESPSS is a free set of plugins for SPSS to provide New Statistics output for common research designs.  This project is still early in development, but you can find modules for 2-group designs and correlations.  The GitHub page is here:
  • R is the Swiss Army Knife of statistics, and you can certainly accomplish New Statistics analysis with R.  It can be a bit tricky, though, to get R to do all you’d like.  Fortunately, David Erceg-Hurn has put together a guide to the New Statistics in R.  You can download the guide here:
    • The ITNS package by David Erceg-Hurn can make the cool plots that show a difference axis, a nice way to show the effect size of interest for a two-group comparison
    • The userfriendlyscience package in R has lots of cool functions for New Statistics analysis and outpu
  • Jamovi is a promising free, open-source statistics program that provides a user-friendly interface for R.  We don’t have any specific New Stats commands for Jamovi yet, but hope to develop some soon.
  • Jasp is a free, open source statistics program with an emphasis on Bayesian analysis.  It will produce credible intervals for many types of designs, the Baysian version of a confidence interval.

Sample-Size Planning (Planning for Precision)

  • ESCI provides worksheets for planning two-group designs for precision
  • Some other good planning for precision tools are:
  • For the Bayesian approach, see Doing Bayesian Data Analysis (J. Kruschke K., 2014)
  • There is a fantastic set of articles on planning for precision by Ken Kelley and associates
    • A general overview of the approach (Maxwell, Kelley, & Rausch, 2008)
    • For regression analysis (Kelley & Maxwell, 2003)
    • For mediator analysis (Kelley, 2007)


  • ESCI provides some pretty good tools for meta-analysis.  It enables meta-analysis of raw scores or standardized effect sizes.  It enables subgroup analysis.
  • Open Meta Analyst is a free, open source GUI for the metafor package.  It provides meta-analysis for several different types of designs, subgroup analysis, and even meta-regression.  It’s pretty easy to use, though it doesn’t seem to be in development any longer.
  • Meta Essentials is a free Excel workbook that makes many types of meta-analysis straightforward.
  • The metafor package for R is amazing.  It’s not very hard to use, and there is a very lively listserv where you can pose questions.  It’s worth just rolling up your sleeves and learning how to use it.

Open Science:

  • The Open Science Framework is your one-stop shop for pre-registration, data sharing, publishing your file drawer and more.
    • The Open Science Framework has developed a pre-registration template that walks you through the pre-registration process. You can download this template here.
    • If you’d like to see what a completed template looks like, here is an example that plans a replication of a study from Ottati et al. (2015).
    • At of 2017 you can also win $1,000 for pre-registering a study with OSF and then publishing the study in a participating journal.
  • Some of the initiatives towards Open Science going on in cognitive neuroscience (Gilmore, Diaz, Wyble, & Yarkoni, 2017)


Copy of ESCI for IU demo: ESCI-intro-chapters-10-16-IU


Cooper, H., Hedges, L., V., & Valentine, J., C. (2009). The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation.
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017). Progress toward openness, transparency, and reproducibility in cognitive neuroscience. Annals of the New York Academy of Sciences, 1396(1), 5–18.
Kelley, K. (2007). Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach. Behavior Research Methods, 39(4), 755–766.
Kelley, K., & Maxwell, S. E. (2003). Sample Size for Multiple Regression: Obtaining Regression Coefficients That Are Accurate, Not Simply Significant. Psychological Methods, 8(3), 305–321.
Kruschke, J., K. (2014). Doing Bayesian Data Analysis. Academic Press.
Kruschke, J. K., & Liddell, T. M. (2017). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4.
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample Size Planning for Statistical Power and Accuracy in Parameter  Estimation. Annual Review of Psychology, 59(1), 537–563.
Pek, J., & Flora, D. B. (2017). Reporting Effect Sizes in Original Psychological Research: A Discussion and Tutorial. Psychological Methods.
Smithson, M. (2000). Statistics with Confidence. SAGE.
Smithson, M. (2003). Confidence Intervals. SAGE.