There are some other points to consider. First, the dataset sizes for most biomedical research are very small. Most advanced statistical methods don't apply. Due to curiosity, I took some advanced stat courses and tried to apply the methods to our lab's data. It didn't provide any significant improvement compared to basic ones, like linear regression, logistic regression, etc.
Second, biomedical research is highly collaborative these days. For some research that generate a large amount of data, either the researchers themselves understand statistics very well, or they collaborate with statisticians very closely. There is a field called biostatistics. Most biostatistics professors are either math or stat major, and many of them are adjoint professors in biomedical departments.
Biomedical research is really tedious and time-consuming. The professors I knew when I was doing biomedical research worked more than 60 hours a day, and they wish they had more time. One young woman professor came to the lab at 8am, left at 6pm, spent some time with her 4 children, and came back to lab at 9pm again, and worked until midnight, on every weekday. She brought her children to the lab on Saturday, and worked the whole day. IMHO, it is better for her to focus all her energy on the biomedical part, which she is best at, and collaborate with statisticians.
This is a very important point I think - only in very rare cases have I found my research actively improved by having a more sophisticated method available, and most projects have a statistician as a collaborator already. If not, they're readily available. It benefits a biologist to know what the statistician is talking about, and not just treating the analysis as a black box, but there's a reason we have subject matter experts. Sometimes, someone saying "Make sure to use robust variance" is enough information.
It depends on what research they're doing. It's also quite easy to be led astray and produce poor work by trying to throw the newest, shiniest thing at something when a much more basic technique will do.
For example, for much of the work I do, you could get away with never using anything more sophisticated than ANOVA.
I take the opposite stance - if biologists knew about advanced statistical methods they might be tempted to use them.
The general rule in biology is if you need to use statistics you did the wrong experiment. The reason for this rule is it is all too easy to use clever statistical methods to solve a flawed experimental design.
It should be noted that "Biology" also encompasses fields where you are limited to uncontrolled observational experiments, which often necessitate more advanced methods.
I agree that more advanced statistical methods would be useful. A surprising number of scientists have poor knowledge about statistics whilst being dependent on it to prove their research.
How do you prove that a medicine is safe and effective if not through large scale studies, which you then use statistics to show whether your hypotesus was correct or not?
