### tl;dr

learning statistics with jamovi (`lsj`

for short) is a basic, introductory statistics textbook that presents most of the topics typically seen in an introductory psychology course at undergraduate level. It is completely free to download, use, and adapt — released under a creative commons CC BY-SA 4.0 licence. Although it is geared towards psychology, the content and material is also relevant to other disciplines, for example health sciences and public health. Download `lsj`

over here.

`lsj`

covers: study design, descriptive statistics, data manipulation, basic plots, statistical inference, the theory of hypothesis testing, chi-square tests, t-tests, correlation, regression, and ANOVA. Throughout the text demonstration analyses are shown using jamovi.

`lsj`

is a fork/adaptation of the excellent Learning Statistics with R (LSR) by Danielle Navarro. What’s really neat about LSR, and by extension `lsj`

, is that there are quite a few topics covered in the text that are missed out of most introductory stats textbooks and courses, but that are important for a good initial understanding of statistics. That’s why LSR was chosen as the basis for `lsj`

— for us it covers the stuff that we wish we had found out about when we were first taught stats. For example, the disagreement between Neyman and Fisher about hypothesis testing is mentioned, and there is a detailed explanation of the different types of sums of squares (Types I, II and III) that is key for understanding unbalanced factorial ANOVA. Moreover, the Bayesian / frequentist divide is included, and there is some explanation and demonstration of the Bayesian approach to analysis, as a counter to the fact that just about all the inferential statistics in the book are presented from an orthodox frequentist perspective (which still fits with the tradition and requirements of many undergraduate psychology courses).

Although there is a lot in `lsj`

, any statistics textbook is undoubtedly incomplete; there is just too much to cover. Ours is no exception, it’s a work in progress. If you spot any mistakes, or want to suggest some improvements, then please log an issue on github: https://github.com/davidfoxcroft/jbook/issues.

Plans for the near future include adding material on repeated measures ANOVA, reliability analysis, and factor analysis. But of course there is scope to add even more. If you would like to contribute some updates to the book, or a chapter, then please get in touch via the lsj website. We’ll keep all contributions in a publicly available repository, linked from the website, and will incorporate new material into the book when we update to the next version.

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