There is a slight formatting error on p 434 (that unfortunately changes the meaning of the sentence as a whole), such that the superscript on the "2" is missing:

Edit to clarify:
It looks like this in the 2019 edition of the book; therefore, I presume a formatting error:

Thank you!

under 9.5.1. my should be by
"This would obviously be significant too but my a much larger margin, such that there's really no ambiguity about this at all."

The text below Table 10.6 says the p-value is .182 but the correct p-value is .192.

Some people consider this usage of the word "crippled" to be a slur - consider changing from

Usually, the company sells "student versions" (crippled versions of the real thing) very cheaply, and then they sell full powered "educational versions" at a price that makes me wince.

to

Usually, the company sells "student versions" (limited versions of the real thing) very cheaply, and then they sell full powered "educational versions" at a price that makes me wince.

Hi - in case no one has raised this yet, in the English version there is a spelling error on page 187 in the first line of the second last paragraph:
"Having chosen a test statistic, the next step is to state precisely which values of the test
statistic would cause [is] to reject the null hypothesis, and which values would cause us to keep it."

[is] should be [us]

Thanks for this comprehensive book.

correct link is now https://learningstatisticswithr.com/

Hi again,

Figure 14.6 appears to be the wrong image (see screenshot attached)

Thanks again,

Rob

This page has multiple repetitions of a couple of paragraphs. This looks unsympathetic. I found this in the epilogue, under "Other Ways to Infer". An example of a repeated paragraph: "In other words, instead of assuming the data is (for example) normally distributed, just assume the population looks the same as your sample, then use computers to model the sample distribution for your test statistic, if so the assumption is correct Despite the somewhat dubious assumption (i.e. the distribution of the population is the same as the sample), bootstrapping is a quick and easy technique that works remarkably well in practice for many data analysis problems. results of your experiment many, many times over, based on the double assumptions that (a) the null hypothesis is true and (b) the unknown population distribution is actually very similar to your raw data.". Several times in a row.

Figure 12.4. does not seem to display the plots for the .33 and .66 correlations.

I am on MAC OS and i am unable to download the textbook. I click the download button and nothing happens

Footer

"I know this is a bot of a fudge..."

Comment from cesar-aguilar

I am using add-on module "learning statistics with jamovi" in jamovi. The data in the add-on module does not correlate with the data presented in the book chapter "5.2.3 Using box plots to detect outliers".

1. There are no observations with over 300points in the module's data.
2. Figure 5.10 The description says ID 14 and ID 134 are the outliers but the picture shows ID 176 and ID 202.
3. I can not find the file "AFL Margins By Year" in Google Drive, only in the module.

On p.78 we find: "Put simply, kurtosis is a measure of the “pointiness” of a data set, as illustrated in Figure 4.12." Several sources, e.g. Westfall (2014) (titled: "Kurtosis as Peakedness, 1905–2014. R.I.P.") argue that "The incorrect notion that kurtosis somehow measures “peakedness” (flatness, pointiness or modality) of a distribution is remarkably persistent, despite attempts by statisticians to set the record straight. This article puts the notion to rest once and for all. Kurtosis tells you virtually nothing about the shape of the peak - its only unambiguous interpretation is in terms of tail extremity; i.e., either existing outliers (for the sample kurtosis) or propensity to produce outliers (for the kurtosis of a probability distribution)."

I suggest you reconsider the notion of kurtosis as a measure of "pointiness" and consider rephrasing that particular segment.

Hello,

I'm a Masters student learning Jamovi right now. I just wanted to indicate that, in future revisions, it was quite difficult to locate the datasets for the textbook. I feel that making it clearer where the datasets can be downloaded would help other readers of the text in the future. Perhaps just adding a note or link making it clearer would be very helpful.

Thanks for reading!

Best,

• Gabe

Hello
I would like to download the pdf version of the book Learning Statistics with Jamovi but it says there is a 404 error

thanks

Hi,

Thank you very, very much for this incredible textbook!

I noticed that section 14.1.4 (What are our degrees of freedom?) and 14.1.5 (What are our degrees of freedom?) are identical and this appears an accidental duplicate.

Also, the last sentence preceding section 14.1.4 has a spelling error:

"...and so what we’re runnning here is an...."

Thanks again,

Rob

The downloadable .pdf file of the book is written in a very thin and hard to read (and hard for the eyes) font, especially while reading on a screen. Is it possible to include an alternative download (or perhaps change the main download) with an easy to read font, such as the brilliant Atkinson Hyperlegible? Wikipedia, github, official site.

In both chapters 2.1.1 and 1.2 (and possibly elsewhere) 'gender', 'sex', 'male', 'female', 'man', 'woman' are used in a confusing and possibly incorrect way. When speaking about 'sex' it should be implied that it's biological in nature and the only possible answers are 'male', 'female' and variations of 'intersex', whereas when talking about 'gender' it should be implied that it's cultural and/or psychological and it's only about 'woman', 'man', 'nonbinary' (and others).

So: In 2.1.1 Is there a reason for using the confusing 'chromosomal gender' instead of the simple and most commonly used 'sex'? Why is 'female' used to describe 'gender'?

And: In 1.2 for the study about college admissions 'female' and 'woman' are used interchangeably even though they are completely different concepts that cannot be used interchangeably [unless: (1) it has been measured (asked) that no males identify as women, and (2) all females identify as women.].

Edit1: also in 2.7.4 'gender' is used to describe biological terms like 'female' and 'male'

Thanks so much for providing this book. It's a great resource! I think there is a typo on page 348 under "Checking the normality assumption" in the 4th sentence it says "if the Shapiro-Wilk test is not significant (i.e. p<.05)..." and I think it should be "...(i.e. p>.05)...".
Perhaps I misread?

Hello,

first, congratulationsn for the great book and software. I started reading it and I'm enjoying a lot. Unfortunately, reading pdfs on a Kindle e-reader is not a pleasent experience. Can you provide mobi files (and maybe epubs) besides the PDFs.

Thanks a lot,

Kemel Zaidan

label missing

Edit1: Weird, black jeans are visible in the files, maybe it's just a quirk of my .pdf viewer or the dark mode inversion feature?
Edit2: it's not the .pdf viewer nor the inversion, another (browser) .pdf viewer has the same issue:

Edit3: here's how it looks in the files:

First paragraph
"As you can see, what we have today is an odd mishmash of the two. We talk about having
both a null hypothesis and an alternative (Neyman), but usually define the p value in terms
of exreme data (Fisher) ...."

extreme?

Thank you for providing this excellent book. I notice what seems to be an error in the presentation of factorial ANOVA with no interactions allowed. The text says "all we have to do is add therapy as a second fixed factor in the analysis" (see the image, below). Then it presents an ANOVA output table pertaining to a model that does not allow interactions. However, as you're aware (since the book says so later on), jamovi's default behavior is to allow interactions and to include the interaction both in the model and in the output table. Thus, to run a no-interactions-allowed ANOVA, the user would have to play around with the "Model Terms" to exclude the interaction. But this isn't explained in the text.