Many scientists have a hate-love relationship with statistics. Personally, I didn’t like statistics (at all) during my masters degree education [1]. Too theoretical, didn’t see the utility of it. Only when I generated my first data during my PhD research, I started realizing the necessity and power of bio-statistics. Later, I almost really fell in love with statistics after reading Intuitive biostatistics by Harvey Motulsky. This excellent book is written by an author who graduated from medical school; this probably explains why it contains only the most pertinent formulas. I particularly appreciate the book as it really is intuitive; it almost reads like a novel, and you could read it in bed, next to the fireplace with a glass of your favorite wine, or even when you’re on holidays. If you always felt the need to sharpen your basic bio-statistics skills, then this book may be really something for you.

Designing a well working and reliable qPCR assay is a lot of work. Apart from prediction of specificity, the assay should also be screened for possible secondary DNA structures that interfere with efficient amplification (see Figure 1 in ) and for the presence of single nucleotide polymorphisms (SNPs) that impair amplification of the variant allele.

Digital PCR is a nucleic acid molecule counting method with unprecedented resolution, relative sensitivity and accuracy. The template is diluted to such an extent that on average 1 template molecule is present in a PCR reaction, and many different small reactions are run in parallel. By counting the number of positive and negative reactions (hence the term ‘digital’) at the end of the PCR, it is possible to determine the starting number of template molecules in the sample under investigation. In other words, the question of how much nucleic acid target molecules are present in a particular sample is answered by measuring how many miniaturized reactions are positive if template is extremely diluted. Importantly, a Poisson correction needs to be applied on the counts, to correct for the fact that some reactions may contain more than 1 template molecule.

Back in 2002 ( ), we pioneered a new normalization strategy for more accurate normalization of mRNA expression data from RT-qPCR studies. Since then, the use of multiple stably expressed reference genes has become the gold standard method (see also MIQE guidelines in ). Today, more than 11000 papers have cited our seminal paper (according to Google Scholar)!