From significance testing to estimation and Open Science: How esci can help

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 (https://thenewstatistics.com).