Estimation, Open Science, and Bob’s Wonderful New esci

Our open access article just released at


  • Three dramatisations of the enormous unreliability of the p value. Can these help weaken researchers’ addiction to NHST that has withstood more than half a century of cogent rational critiques?
  • Bob’s wonderful new open-source esci software with great estimation-based figures: See worked examples, and work along if you wish.


We argue that researchers should test less, estimate more, and adopt Open Science practices. We outline some of the flaws of null hypothesis significance testing and take three approaches to demonstrating the unreliability of the p value. We explain some advantages of estimation and meta-analysis (“the new statistics”), especially as contributions to Open Science practices, which aim to increase the openness, integrity, and replicability of research. We then describe esci (estimation statistics with confidence intervals): a set of online simulations, and an R package for estimation that integrates into jamovi and JASP. This software provides (a) online activities to sharpen understanding of statistical concepts (e.g., “The Dance of the Means”); (b) effects sizes and confidence intervals for a range of study designs, largely by using techniques recently developed by Bonett; (c) publication-ready visualisations that make uncertainty salient; and (d) the option to conduct strong, fair hypothesis evaluation through specification of an interval null. Although developed specifically to support undergraduate learning through the 2nd edition of our textbook, esci should prove a valuable tool for graduate students and researchers interested in adopting the estimation approach. Further information is at

Figure 1. Significance roulette. If an initial study obtains p=.01, an exact replication–just the same but with an new sample–will obtain a p value drawn from the enormous spread of values on the wheel.

The Enormous Unreliability of p

This is the first time (1) the dance of the p values (search YouTube), (2) significance roulette (Figure 1; and search YouTube), and (3) p intervals (see the article) have all been described together in print. Significance roulette has been around for a while but this is its first outing in print. Alas, p values simply don’t deserve our trust. Enjoy the figures!

esci web

This component of esci is a set of simulations and tools by our colleague Gordon Moore that run in any browser. Explore the dances, play with sampling distributions, find critical values, and more.

esci for Data Analysis

Bob’s esci is an open-source package in R, which can be run in R, or within jamovi or (by December 2024) in JASP. We describe the wide range of measures and designs esci can analyse, including meta-analysis, and work through several examples. We emphasise figures that highlight uncertainty, especially by picturing confidence intervals.

Figure 2. Part of jamovi screen showing selection of the ‘Gender math IAT’ data file for opening.

We argue that p values, if used at all, are most valuable in the context of hypothesis evaluation based on an interval null hypothesis, and best understood with the help of an esci figure–see Figure 3.

Interactions can be challenging to understand and interpret; again esci provides figures designed to help–see Figure 4.

Pro Tip: Data Files Now in esci

The article advises download of jamovi-format data files (Gender math IAT.omvGender math IAT ma.omvCampus Involvement.omv and MeditationBrain.omv) from Since the final version of the article was submitted Bob has integrated into esci all the data files used in ITNS2, including these four, so download from OSF is no longer needed.

Figure 3. esci figure for two independent groups. At left, the data points, means and 95% CIs for the two groups. The black triangle marks the difference between the means. This and its 90% and 95% CIs are shown on the difference axis at right. The two CIs allow test of the interval null hypothesis, the pink stripe.

Examples of Analyses by esci

Figures 3 and 4 are just two illustrations from the example esci analyses discussed in the article.

To open a data file within esci, click top left in jamovi, then click Open, Data Library, and scroll to see all the data files for ITNS2 arranged by chapter. Figure 2 shows selection of the first example file used in the article.

Figure 3 is an esci figure from a two independent groups analysis of the Gender math IAT file. The grey areas on the CIs are what we call plausibility curves. These illustrate variation in the plausibility, or relative likelihood, that values across and beyond the interval are the population value.

Figure 4 is one of the ways esci can display a 2 x 2 interaction–part of an RCT analysis of the MeditationBrain file.

If you wish, work along with the examples. The rich UI (user interface) of esci gives lots of scope to make figures look just as you want them–there’s advice about how to tweak your figures to look like those in the article.

As ever, we’d love to hear your comments on the new book and new software. Enjoy.

Figure 4. One way esci displays a 2 x 2 interaction. The difference in slope of the two lines indicates the size of the interaction. The fans of faint lines give a rough indication of the extent of uncertainty in estimating the slopes of the lines.


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