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Probably, the real problem is that "the scientific literature should be". It's not the right question to discuss today, because scientific literature is just a

Probably, the real problem is that "the scientific literature should be". It's not the right question to discuss today, because scientific literature is just a brief approximation of scientific knowledge and this is not what we need. Whole modern scientific system is outdated and has to be upgraded: journals, citation indexes, degrees should go away and be replaced. Science should become more formal and more digital, so that it will be easier to find and validate the results. There should be databases, search engines and digital signatures of the raw data and chains of proofs. Counting citations should be replaced with counting chains of proof produced by the scientist and included in others' works. Reliability of proof and contribution to ratings should be based on independent confirmations, which should be counted too - replication of new results should be considered an achievement too. Journals may remain, but not as a primary way to exchange scientific information - more likely, they should become the portals to the data which will link the results in databases to their human-readable interpretations.

entee 10y ago
Respectfully, I think you're missing the complications outlined in the rest of the thread. The difficulty in the biosciences and even in chemistry and some physics is that they can't really be formalized in the way that is necessary to construct these "chains of proof". The scientific literature is an ongoing conversation anchored by rigorous experimental facts and data. But rigorous doesn't mean it's clean like a mathematical proof. In fact, most science approaches its "proofs" in quite a different way than math. For example, as far as science would be concerned P!=NP in complexity theory. We've done the experiment many times, tried different things, it's pretty much true. But it's still not mathematically proven because there isn't a formal proof. That's not to say it's invalid to expect more rigor, or that we wouldn't all love to have "chains of proof" and databases and signatures for data etc. It's that it's simply not practical given how noisy and complex biological systems are. In contrast to math, you pretty much never know the full complement of objects/chemicals/parameters in your experimental space. You try to do the right controls to eliminate the confounding variables, but you're still never fully in control of all the nobs and switches in your system. That's why usually you need multiple different experiments tackling a problem from multiple different approaches for a result to be convincing. Formalized systems would be great, but I don't think we're even close to understanding how to properly formalize all of those difficulties and variables in a useful way. And it may not even be possible.
- dorgo 10y ago
  I thought that statistics is the right tool to handle uncertainity? I had the impression that all the "soft" science is based on statistics. We can't prove that smoking kills people. But, given a certain confidance interval (or whatever equivalent measure for baysian statistics?), we can state that smoking is not a good idea if you want to live longer than mean expected years... Sure, some unforseen event may interfere with your experiment. But statistics should account for that, shouldn't it? Just wondering..
  - epistasis 10y ago
    Statistics is also the way to look at data in the "hard" sciences. Every measurement has error, and that's how you deal with it. However it's not a simple thing that automatically combines different studies. It takes skilled application to understand how data connects, what's comparable, what's not, etc. Traditionally, "meta-analysis" is the sub-field of statistics that combines studies. But that only combines extremely simple studies, such as high-controlled clinical trials. It's inappropriate for the complex type of data that appears in a typical molecular biology paper is a chain of lots of different types of experimental setups. Those who don't know the body of stats, trying to reason about application, is a lot like an MBA trying to reason about software architecture. The devil is in the details, and the details are absolutely 100% important with application of statistics to data.
    - Balgair 10y ago
      Amen. You can google the controversy around the StatCheck program to really dive into why stats and their applications are beyond lies and damned-lies in their falsehoods (because you can prove the lie is right). Doing something as simple as smoking causes cancer is a very very simple experiment to interpret (hard to fund/preform though). It's the pernicious little studies on 8 cells total that get messy. Preforming a lot of experiments is hard. I knew of a student that graduated with only 8 cells of data out of his 7 years in grad school. That may sound like a small amount, but to his committee, it was a very impressive number. He (very very) basically sliced up adult rodent brains and then used super tiny glass pipettes to poke the insides of certain cells. He chemically altered these cells' insides in a hopefully intact network of neurons, shocked the cells, and then recorded the activity of other cells in the network using the same techniques. Then he preserved and stained the little brain slice so he could confirm his results anatomically. From start to finish, it took him 13 hours total, no lunch or restroom breaks, every day, for 7 years. He got 8 confirmable cells worth of recordings total. That is a hard experiment. But due to his efforts in adding evidence we now suspect that most adult hearing loss is not due to loss of cells in the ear, but in the coordination of signals to the brain and their timing mis-match. It is not much, bu it adds to the evidence and will for sure help out people someday. To add, he is now a beer brewer in Bavaria and quit science. This shit takes sacrifice man.

entee 10y ago

Respectfully, I think you're missing the complications outlined in the rest of the thread. The difficulty in the biosciences and even in chemistry and some physics is that they can't really be formalized in the way that is necessary to construct these "chains of proof". The scientific literature is an ongoing conversation anchored by rigorous experimental facts and data. But rigorous doesn't mean it's clean like a mathematical proof. In fact, most science approaches its "proofs" in quite a different way than math. For example, as far as science would be concerned P!=NP in complexity theory. We've done the experiment many times, tried different things, it's pretty much true. But it's still not mathematically proven because there isn't a formal proof. That's not to say it's invalid to expect more rigor, or that we wouldn't all love to have "chains of proof" and databases and signatures for data etc. It's that it's simply not practical given how noisy and complex biological systems are. In contrast to math, you pretty much never know the full complement of objects/chemicals/parameters in your experimental space. You try to do the right controls to eliminate the confounding variables, but you're still never fully in control of all the nobs and switches in your system. That's why usually you need multiple different experiments tackling a problem from multiple different approaches for a result to be convincing. Formalized systems would be great, but I don't think we're even close to understanding how to properly formalize all of those difficulties and variables in a useful way. And it may not even be possible.

dorgo 10y ago
I thought that statistics is the right tool to handle uncertainity? I had the impression that all the "soft" science is based on statistics. We can't prove that smoking kills people. But, given a certain confidance interval (or whatever equivalent measure for baysian statistics?), we can state that smoking is not a good idea if you want to live longer than mean expected years... Sure, some unforseen event may interfere with your experiment. But statistics should account for that, shouldn't it? Just wondering..
- epistasis 10y ago
  Statistics is also the way to look at data in the "hard" sciences. Every measurement has error, and that's how you deal with it. However it's not a simple thing that automatically combines different studies. It takes skilled application to understand how data connects, what's comparable, what's not, etc. Traditionally, "meta-analysis" is the sub-field of statistics that combines studies. But that only combines extremely simple studies, such as high-controlled clinical trials. It's inappropriate for the complex type of data that appears in a typical molecular biology paper is a chain of lots of different types of experimental setups. Those who don't know the body of stats, trying to reason about application, is a lot like an MBA trying to reason about software architecture. The devil is in the details, and the details are absolutely 100% important with application of statistics to data.
  - Balgair 10y ago
    Amen. You can google the controversy around the StatCheck program to really dive into why stats and their applications are beyond lies and damned-lies in their falsehoods (because you can prove the lie is right). Doing something as simple as smoking causes cancer is a very very simple experiment to interpret (hard to fund/preform though). It's the pernicious little studies on 8 cells total that get messy. Preforming a lot of experiments is hard. I knew of a student that graduated with only 8 cells of data out of his 7 years in grad school. That may sound like a small amount, but to his committee, it was a very impressive number. He (very very) basically sliced up adult rodent brains and then used super tiny glass pipettes to poke the insides of certain cells. He chemically altered these cells' insides in a hopefully intact network of neurons, shocked the cells, and then recorded the activity of other cells in the network using the same techniques. Then he preserved and stained the little brain slice so he could confirm his results anatomically. From start to finish, it took him 13 hours total, no lunch or restroom breaks, every day, for 7 years. He got 8 confirmable cells worth of recordings total. That is a hard experiment. But due to his efforts in adding evidence we now suspect that most adult hearing loss is not due to loss of cells in the ear, but in the coordination of signals to the brain and their timing mis-match. It is not much, bu it adds to the evidence and will for sure help out people someday. To add, he is now a beer brewer in Bavaria and quit science. This shit takes sacrifice man.