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44 Strings 6%
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5,960 Words 1%
35,037 Characters 1%

Strings status

Strings Words Characters
44 5,960 35,037
All strings Browse Translate Zen
3 3 24
Translated strings Browse Translate Zen
41 5,957 35,013
Unfinished strings Browse Translate Zen
41 5,957 35,013
Untranslated strings Browse Translate Zen
41 5,957 35,013
Unfinished strings without suggestions Browse Translate Zen

Overview

Project website lsj.readthedocs.io
Instructions for translators

Dear translators,

welcome and thank you for your willingness to contribute to the translation of the “learning statistics with jamovi”-text book!

There are a couple of things that I would like to ask you, in order to make your and my life a bit easier (manual corrections need time, so that what you translated won't appear until I fixed it): [1] There are buttons underneath the “Translation” box. One of them is “Automatic suggestions” which provides you with a machine translation of the text to be translated. This can just be copied into the target field for the translation and adjusted. [2] There are a couple of text elements that should be treated different from the rest of the text (please check: the automatic suggestions might translate this “wrong”): - placeholders (marked by “|”) should not be translated: e.g., |placeholder| or |placeholder|_, among them is |continuous| which produces an icon denoting a continuous level of measurement - double backticks : they typically are used for variable or analysis names – the variable names shouldn't be changed, for the analysis names, it is up to your discretion whether to translate them or not (I decided to rather go with the English analysis since the feature to set the UI language in jamovi is fairly new and it is relatively simple to figure out the translated name from the English one) - be careful that :doc: does not contain spaces at the begin of the string, also ensure to leave the part enclosed by <> as it is (it points to a file; therefore, please double-check that the automatic translation did not translate it otherwise it will end up as a broken link) - figure references, e.g., :numref:fig-normAreaSD should not to be translated - (language-specific) quotation marks: there are buttons above the text field for the target translation that can be used to replace "'s by your language specific quotation marks [3] markdown-formattings for italicsor bold: please ensure that the stars are placed around the same word / meaning in the translation as in the original [4] if there doesn't exist a good translation of a particular word or technical term, either try "translation (original word / phrase)" or "original word / phrase" (this shows the original English term in italics either with a translation that you may not be completely happy with or without a translation

Finally, I would like to suggest that you install LanguageTool (browser add-on) as an additional check that your translations are correct (however, LanguageTool might not be available for every language).

Don't hesitate to contact me (@sjentsch) if you have questions or suggestions.
Project maintainers User avatar sjentsch
Translation license Creative Commons Attribution Share Alike 4.0 International
Translation process
  • Translations can be made directly.
  • Translation suggestions can be made.
  • Any authenticated user can contribute.
  • The translation uses bilingual files.
Source code repository git@github.com:sjentsch/lsjDocs-i18n.git
Repository branch main
Last remote commit Translated using Weblate (Chinese (Traditional Han script)) b2f6c0ae
hugoalh authored 3 weeks ago
Last commit in Weblate Translated using Weblate (Chinese (Traditional Han script)) b2f6c0ae
hugoalh authored 3 weeks ago
Weblate repository https://hosted.weblate.org/git/lsjdocs/start/
File mask */LC_MESSAGES/Ch17.po
Translation file Download it/LC_MESSAGES/Ch17.po
Last change Aug. 13, 2024, 7 p.m.
Last change made by None
Language Italian
Language code it
Text direction Left to right
Number of speakers 69,298,348
Number of plurals 2
Plural type One/other
Plurals Singular 1
Plural 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, …
Plural formula n != 1
3 days ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 44 5,960 35,037
Translated 6% 3 1% 3 1% 24
Needs editing 0% 0 0% 0 0% 0
Read-only 0% 0 0% 0 0% 0
Failing checks 0% 0 0% 0 0% 0
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 93% 41 99% 5,957 99% 35,013
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Quick numbers

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Resource updated
The “it/LC_MESSAGES/Ch17.po” file was changed. 3 months ago
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*“and go on till you come to the end: then stop”*
 
3 months ago
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Resource updated
The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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Resource updated
The “it/LC_MESSAGES/Ch17.po” file was changed. a year ago
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String updated in the repository
**Power analysis.** In chapter :doc:`../Ch09/Ch09_HypothesisTesting` I discussed the concept of power (i.e., how likely are you to be able to detect an effect if it actually exists) and referred to power analysis, a collection of tools that are useful for assessing how much power your study has. Power analysis can be useful for planning a study (e.g., figuring out how large a sample you’re likely to need), but it also serves a useful role in analysing data that you already collected. For instance, suppose you get a significant result, and you have an estimate of your effect size. You can use this information to estimate how much power your study actually had. This is kind of useful, especially if your effect size is not large. For instance, suppose you reject the null hypothesis at *p* < 0.05, but you use power analysis to figure out that your estimated power was only 0.08. The significant result means that, if the null hypothesis was in fact true, there was a 5\% chance of getting data like this. But the low power means that, even if the null hypothesis is false and the effect size was really as small as it looks, there was only an 8\% chance of getting data like you did. This suggests that you need to be pretty cautious, because luck seems to have played a big part in your results, one way or the other!
 
a year ago
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String updated in the repository
**Bootstrapping.** Throughout the book, whenever I’ve introduced a hypothesis test, I’ve had a strong tendency just to make assertions like “the sampling distribution for BLAH is a *t*-distribution” or something like that. In some cases, I’ve actually attempted to justify this assertion. For example, when talking about χ²-tests in chapter :doc:`../Ch10/Ch10_ChiSquare`, I made reference to the known relationship between normal distributions and χ²-distributions (see chapter :doc:`../Ch07/Ch07_Probability` to explain how we end up assuming that the sampling distribution of the goodness-of-fit statistic is χ². However, it’s also the case that a lot of these sampling distributions are, well, wrong. The χ²-test is a good example. It is based on an assumption about the distribution of your data, an assumption which is known to be wrong for small sample sizes! Back in the early 20th century, there wasn’t much you could do about this situation. Statisticians had developed mathematical results that said that “under assumptions BLAH about the data, the sampling distribution is approximately BLAH”, and that was about the best you could do. A lot of times they didn’t even have that. There are lots of data analysis situations for which no-one has found a mathematical solution for the sampling distributions that you need. And so up until the late 20th century, the corresponding tests didn’t exist or didn’t work. However, computers have changed all that now. There are lots of fancy tricks, and some not-so-fancy, that you can use to get around it. The simplest of these is bootstrapping, and in it’s simplest form it’s incredibly simple. What you do is simulate the results of your experiment lots and lots of times, under the twin assumptions that (a) the null hypothesis is true and (b) the unknown population distribution actually looks pretty similar to your raw data. In other words, instead of assuming that the data are (for instance) normally distributed, just assume that the population looks the same as your sample, and then use computers to simulate the sampling distribution for your test statistic if that assumption holds. Despite relying on a somewhat dubious assumption (i.e., the population distribution is the same as the sample!) bootstrapping is quick and easy method that works remarkably well in practice for lots of data analysis problems.
 
a year ago
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