Genomic data science as a tool to biologist

A concept that I believe to crack the bottle neck of connecting NGS and conventional science

I believe that science is all about hypothesis testing. But the hypothesis that I am talking about here is a bit different from the way statisticians would like to put. For example,

Wait a second, what's the deal here? They could be essentially the same at some level. So the criticality lies in how to validate the conclusion. For the former I might want to perform any sort of quantifiable PCR to validate the expression, but for the latter I would like to see what if I use other treatment and what if I assess at different molecular level such as protein and/or other clinically/physiologically related phenotype.

The way you phrase your hypothesis dictates your research directions and methods, and thus output.

I am not saying who is better than who, I am saying that we should all work together. In my journey of self-educating to use the NGS tools, Mathematicians tends to create new terminology to suit their needs on some well-known "things", such as the "summarizing transcriptome to gene level" conversion used widely in DESeq2 related discussion. So sometimes I am also confused, in which line of professions or methodology that I am using to solve a natural science question which is universal to every single living things on the Earth. I mean, should I even need to worry about that when if we all are heading to the same Rome?