Earlier quoted context omitted.
For those not too familiar with professional baseball, there are 162 games in a season. A player starting every game would likely see 600+ at bats. A starting pitcher pitches every 5-6 days and will face 600+ batters per season. League wide there are about 125k outs recorded per season on 700k pitches. That's a ton of data. It allows you to ask questions like "What is the likelihood that a curveball is thrown for a s…
> It allows you to ask questions like "What is the likelihood that a curveball is thrown for a strike on a 3-0 count in the 8th inning?" and get statistically significant results. Does it though? medicine is learning that when you do data mining the bar for statistical significance needs to be much higher. When you do the traditional hypothesis/experiment loop and a result hits 95% likely that is good enough. However…
Biological systems are highly variable and absurdly complex, and most datasets come with a host of confounding factors. In comparison, baseball is extremely uniform, and the variability that does exist can often be quantified effectively, and more importantly, often is. Biologists can dream of such high-quality and thorough data, but that's a long way off. This means that the analysis used is quite different.
Genomic data depends greatly on the material used and the methods used. For instance, even with consistent genotypes and identical library preparation, if you collected your RNA a few hours later in the day, you now have a host of circadian changes to contend with that confound your analysis. No one can effectively keep track of all the confounding factors. This means that most analysis needs to be done with direct controls, biological replicates, etc.
In terms of actual analysis, I think the problem is somewhat overstated in your assessment. There are good statistical methods to adjust for multiple comparisons, and the field has largely caught-up to the biggest issues. This was perhaps more accurate 5 years ago, and was mostly the result of poor statistical literacy.