text:   Variances",6,"['New paper! We derive default Bayes factors for testing hypotheses on independent population variances: <LINK>\n\n@donvdbergh @AlexanderLyNL @EJWagenmakers (1/6) <LINK>', 'We choose a prior so that the resulting Bayes factor fulfills a number of desiderata. This leads to a closed-form expression for the K = 1 and K = 2 group case. (2/6) https://t.co/5wpxZ167k9', 'It is arguably more intuitive to reason about standard deviations than about variances, and we provide practical examples from psychology and paleoanthropology (cool, right?). One may also elicit an informative prior rather than rely on defaults. https://t.co/Ml5PwJAPyq', ""We extend out Bayes factor to K &gt; 2 groups and allow testing 'mixed' or 'informative' hypothesis (see left image). We provide practical examples from archeology (chupa pots!) and education. https://t.co/ritexKthmG"", 'Our default Bayes factor generalizes a recently proposed automatic fractional Bayes factor (Böing-Messing &amp; Mulder, 2018), yielding the exact same results when testing hypotheses about (in)equality when choosing a rather wide prior \\alpha = 0.50. https://t.co/b0PcNKYhxz', ""All of this is implemented in the R package 'bfvartest', which is available from Github (https://t.co/jHuVZdQPaE). Give it a go! Of course, this will also be implemented in @JASPStats for ease of use :-) https://t.co/8pk63VkbkW""]",20,03,1240