One thing I love about is that you always new things, even when giving a lecture about stuff that you know (or you think you do) very well.

Today a student asked why we should use Student's -test at all when comparing samples with equal variances if you can use Welch's variant, which works with unequal variances... that is, why bother checking equality of variances?

Indeed, in the case of equal variances the calculated t statistics would be the same!

This led to a great discussion involving 3 instructors, a bunch of students, and a few simulations in which I really enjoyed!

The answer is that Welch's variation also changes how degrees of freedom are calculated, so no they are not equal!

Also, the current recommendation is NOT to check for variance equality (e.g. using Levene's test) as this paper clearly points out, lest you want to increase your Type I error!

bpspsychub.onlinelibrary.wiley

Gotta go change some slides...

I have created a little R interface to deconvolute assay data.

This is based on Brown 2008 - Inferring gene expression dynamics from reporter protein levels (but does not include volume correction)

The app also reports initial rate, maximum and decay rate

It is available here, for anyone to enjoy, modify etc

github.com/nicolaromano/LucDec

Just learned you can vertically split the in which is extremely useful to have a second terminal to do some on-the-fly tests while running a Shiny app!

Hello everyone! Here's my ! I am a senior lecturer at the University of Edinburgh, mostly teaching biomedical and ( and all that jazz) using and . My research interests revolve around that is, understanding how your brain controls the production of hormones in your body. I am particularly interested in the role of of cells in the gland and how that contributes to generating the right pattern of hormones at the right time!

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