4 ms·
It's a bit of a ramble, but it's hardly incoherent. What seem to like diverse topics to a physicist are actually not all that diverse when looking from a holist
by blix 7y ago
It's a bit of a ramble, but it's hardly incoherent. What seem to like diverse topics to a physicist are actually not all that diverse when looking from a holistic perspective. It's not that scientists are sitting on huge piles of data and mining them for interesting results without testing hypotheses (though we are doing exactly this in many domains), it's that the fundemental approach to statistical analysis is flawed.
With enough data, good enough filters, and a wide selection of adjustments for background processes, any model can be made to work. Putting the blocks together is fundementally an excercise in bias, and truly limiting this bias requires significant discipline that is highly disincentivized and therefore uncommon.
The way she writes invites dismissal from working scientists due to its imprecise and conversational style, but I think she makes many salient points about the flaws in the modern scientific institution.
- ghgfhgfhn 7y agoYeah, she has a kind of intelligence that goes beyond precision and rigour. It reaches out in into a holistic space. I got the feeling, reading her writing that she was way, way smarter than I am. Which, blowing my own trumpet, is not that common for me.
- amadsen 7y ago> the fundemental approach to statistical analysis is flawed This is a very strong assertion. What changes do you think are needed? > With enough data, good enough filters, and a wide selection of adjustments for background processes, any model can be made to work. Sure. Which is why no scientist will care that a particular signal model "can be made to work". You try everything you can to explain your data with only background processes and only if this fails do you consider alternatives. The more adjustments you allow, the harder it becomes to favor signal over background. > Putting the blocks together is fundementally an excercise in bias, and truly limiting this bias requires significant discipline that is highly disincentivized and therefore uncommon Another wild accusation without evidence. Why do you believe this is true? And if it is, where are all the false discoveries? In a small team with less oversight, I'm sure cutting corners happens. In a large experiment like those discussed here? No way. The embarrassment of having to retract a false discovery is a pretty strong incentive to ensure the integrity of results, and they have enough internal controls to enforce it.
- blix 7y agoIt's not a "wild accusation" or personal attack, it's a fundemental truth about the nature of modeling. You can do better against it if you start off by recognizing that it is there. Nothing is unbiased. The embarassment of retraction is also a strong incentive to not challenge the status quo. If everyone is using the same bias and assumptions no one has to worry about retraction. The more small teams you have, the easier it is for different biases to live; the more centralized, the easier it is to succumb to groupthink.