## LESSWRONGLW

Bayes is referenced a lot in LW, and for good reason but Bayes theorem is not all that difficult to understand particularly for someone with your education.

The point isn't understanding Bayes theorem. The point is methods that use Bayes theorem. My own statistics prof said that a lot of medical people don't use Bayes because it usually leads to more complicated math.

The most useful skill a knowledge of statistics can give you, arguably, is being able to objectively analyse and comprehend extremely large amounts of data.

That's not the skill that's... (read more)

The point isn't understanding Bayes theorem. The point is methods that use Bayes theorem. My own statistics prof said that a lot of medical people don't use Bayes because it usually leads to more complicated math.

To me, the biggest problem with Bayes theorem or any other fundamental statistical concept, frequentist or not, is adapting it to specific, complex, real-life problems and finding ways to test its validity under real-world constraints. This tends to require a thorough understanding of both statistics and the problem domain.

That's not the ski

1byerley5y"My own statistics prof said..." I am sure we sure we are more than capable of looking beyond the scope of what your statistics professor had time to teach you at university. I have some knowledge and education of statistics myself, not that it makes me particularly more entitled to comment about it. "Thats not the skill that's taught in a statistics degree." I commend you for apparently having a statistics degree of some form. To suggest that analysing and comprehending large amounts of data isnt taught in a statistics degree makes me question your statistics degree. I'm not saying your degree is any better worse, perhaps just unique. Of course, comprehending large amounts statistical data would lead to the use of algorithms to accurately explain the data. We rely on algorithms and mathematics for statistical analysis. Understanding the 'complicated' maths or Bayes theorem wouldnt seem like that great a stretch given the OP's education which is my initial point.

# 14

• A Ph.D. in a biological science, with a fair amount of reading and wet-lab work under your belt on the topic of aging and longevity (but in hindsight, nothing that turned out to leverage any real mechanistic insights into aging).
• A M.S. in statistics. Sadly, the non-Bayesian kind for the most part, but along the way acquired the meta-skills necessary to read and understand most quantitative papers with life-science applications.
• Love of programming and data, the ability to learn most new computer languages in a couple of weeks, and at least 8 years spent hacking R code.
• Optimistically, two decades remaining in which to make it all count.

Imagine that your goal were to slow or prevent biological aging...

1. What would be the specific questions you would try to tackle first?
3. How would you allocate your limited time between the research questions in #1 and the acquisition of new skills in #2?

Update

I thank everyone for their input and apologize for how long it has taken me to post an update.

I met with Aubrey de Grey and he recommended using the anonymized patient data to look for novel uses for already-prescribed drugs. He also suggested I do a comparison of existing longitudinal studies (e.g. Framingham) and the equivalent data elements from our data warehouse. I asked him that if he runs into any researchers with promising theories or methods but for a massive human dataset to test them on, to send them my way.

My original question was a bit to broad in retrospect: I should have focused more on how to best leverage the capabilities my project already has in place rather than a more general "what should I do with myself" kind of appeal. On the other hand, at the time I might have been less confident about the project's success than I am now. Though the conversation immediately went off into prospective experiments rather than analyzing existing data, there were some great ideas there that may yet become practical to implement.

At any rate, a lot of this has been overcome by events. In the last six months I realized that before we even get to the bifurcation point between longevity and other research areas, there are a crapload of technical, logistical, and organizational problems to solve. I no longer have any doubt that these real problems are worth solving, my team is well positioned to solve many of them, and the solutions will significantly accelerate research in many areas including longevity. We have institutional support, we have a credible revenue stream, and no shortage of promising directions to pursue. The limiting factor now is people-hours. So, we are recruiting.

Thanks again to everyone for their feedback.