“A M.S. in statistics. Sadly, the non-Bayesian kind for the most part”
I’d hardly be ashamed of having a ‘non-Bayesian’ statistics degree. 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 most useful skill a knowledge of statistics can give you, arguably, is being able to objectively analyse and comprehend extremely large amounts of data.
Have you looked into the possibility of acquiring a research partner? It may be a more effective use of your time to predominantly take care of the statistical analysis and the biological experimentation while your partner (endowed with skills you don’t have time to learn yourself) can present fresh ideas for new research. This method would be prone to less bias and if it’s a race against time, you may not have enough to acquire an entirely new skill set.
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 taught in a statistics degree. Dealing with large amounts of biological data needs algorithms and quite often Bayes somewhere.
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 skill that’s taught in a statistics degree.
Not explicitly, no. My only evidence is anecdotal. The statisticians and programmers I’ve talked to appear to overall be more rigorous in their thinking than biologists. Or at least better able to rigorously articulate their ideas (the Achilles heel of statisticians and programmers is that they systematically underestimate the complexity of biological systems, but that’s a different topic). I found that my own thinking became more organized and thorough over the course of my statistical training.
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.
I have studied bioinformatics and as such I have a particular idea of the domain of medical statistics and the domain of bioinformatics.
Big data often means that testing for 5% significance is a bad idea. As a result people working on big biological data weren’t very welcome by the frequentists in medical statistics and bioinformatics formed it’s own community.
That community split produces effects such as bioinformatics having it’s own server for R packages and not using the server in which the statistics folks put their R packages.
In another post in this thread bokov speaks of wanting to use Hidden Markov Models (HMM) for modeling. HMM is the classic thing that based on Bayes rule and that people in bioinformatics use a lot but that’s not really taught in statistics.
Understanding the ‘complicated’ maths or Bayes theorem
Understanding Bayes theorem is not hard. Bayes theorem itself is trivial to learn. Understanding some complex algorithm for determining Hidden Markov Models based on Bayes rule is the harder part.
Machine Learning is also a different community then standard statistics. It’s also not only about Bayes theorem. There are machine learning algorithms that don’t use Bayes. Those algorithms are still different than what people usually do in statistics.
“A M.S. in statistics. Sadly, the non-Bayesian kind for the most part”
I’d hardly be ashamed of having a ‘non-Bayesian’ statistics degree. 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 most useful skill a knowledge of statistics can give you, arguably, is being able to objectively analyse and comprehend extremely large amounts of data.
Have you looked into the possibility of acquiring a research partner? It may be a more effective use of your time to predominantly take care of the statistical analysis and the biological experimentation while your partner (endowed with skills you don’t have time to learn yourself) can present fresh ideas for new research. This method would be prone to less bias and if it’s a race against time, you may not have enough to acquire an entirely new skill set.
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.
That’s not the skill that’s taught in a statistics degree. Dealing with large amounts of biological data needs algorithms and quite often Bayes somewhere.
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.
Not explicitly, no. My only evidence is anecdotal. The statisticians and programmers I’ve talked to appear to overall be more rigorous in their thinking than biologists. Or at least better able to rigorously articulate their ideas (the Achilles heel of statisticians and programmers is that they systematically underestimate the complexity of biological systems, but that’s a different topic). I found that my own thinking became more organized and thorough over the course of my statistical training.
“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.
I have studied bioinformatics and as such I have a particular idea of the domain of medical statistics and the domain of bioinformatics.
Big data often means that testing for 5% significance is a bad idea. As a result people working on big biological data weren’t very welcome by the frequentists in medical statistics and bioinformatics formed it’s own community.
That community split produces effects such as bioinformatics having it’s own server for R packages and not using the server in which the statistics folks put their R packages.
In another post in this thread bokov speaks of wanting to use Hidden Markov Models (HMM) for modeling. HMM is the classic thing that based on Bayes rule and that people in bioinformatics use a lot but that’s not really taught in statistics.
Understanding Bayes theorem is not hard. Bayes theorem itself is trivial to learn. Understanding some complex algorithm for determining Hidden Markov Models based on Bayes rule is the harder part.
Machine Learning is also a different community then standard statistics. It’s also not only about Bayes theorem. There are machine learning algorithms that don’t use Bayes. Those algorithms are still different than what people usually do in statistics.