[Obi_Juan_Kenobi]

My impression with experience in bioinformatics is that these issues are likely not shared.

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.