Anyone who follows this blog must get the impression that I am a nag and misanthropist. Nothing and nobody seems to please me. I alway find the sample sizes too small, the statistics too lazy, the data hand-picked, or the results too positive and the conclusions drawn from them too exaggerated. Also, I find the peer review system unreliable, not to mention support of mainstream research and giving to those who already have (‘Matthew effec’)  by the funding agencies. I even dared to critsize the Nobel Prize an atavistic instrument celebrating the lonely, white male reseacher and genius. Artificial intelligence I find stupid, and the academic career system the core of all these evils. To name but just a few examples.

But that is far from the truth! I am a science enthusiast! I am convinced that science is the best that the 1500 grams of protein and fat encapsulated in our skull have ever produced. Yes, I am a science nut. So with this entry, at the beginning of the new decade, let’s start with a proper hymn to biomedical science. Continue reading →

Link to my recent webinar hosted by eLIFE (ca. 60 min):

Damn! What an effort: Generation of a knockout mouse line, back crossing in background strain and litermates, all the genotyping. Followed by a plethora of experiments in a disease model: surgery, magnetic resonance imaging, histology, behavioral studies, and so on. Finally the result: No phenotype! The knockout mouse appears to be a mouse like any other. Not different from the wild type background strain. But wait, we rather need to phrase it like this: We did not find a statistically significant difference between knockout and wild type. So we cannot even conclude that wild type are like knowout mice, but rather: If there is a difference, it might be smaller than the detectable effect size, depended on sample size, error level (alpha and beta) and the variance of our results. But we had planned our experiments well:  The sample size was determined a priori, and chosen so that we would have been able to detect a difference on the order of one standard deviation. This is what statisticians call a Cohen’s d of 1, which is considered a substantial effect. We could not have done more animals than the  (34!), because of limited ressources, the duration of the PhD thesis, and the timing of the grant. But what now? Write a paper? Reporting a NULL result? How would this look like in a resume, besides, who cares about NULL results, and which reputable journal would publish them at all? Continue reading →