tag:blogger.com,1999:blog-1812431336777691886.post3092135104556420049..comments2024-08-13T11:52:29.055-04:00Comments on The Mermaid's Tale: Occasionality (vs Probability)?Anne Buchananhttp://www.blogger.com/profile/09212151396672651221noreply@blogger.comBlogger8125tag:blogger.com,1999:blog-1812431336777691886.post-81243841845960881132015-02-19T09:48:28.805-05:002015-02-19T09:48:28.805-05:00Oops, I pushed published when I first meant to pre...Oops, I pushed published when I first meant to press review. I see two typos in the last two sentences:<br /><br />"So I guess that means I believe in the reality of probability while acknowledging that calculating it ahead of time in many cases [is] impossible. This is a sum of part of my take [on] mathematical platonism."James Goetzhttps://www.blogger.com/profile/02412501436355228925noreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-70175495808579786072015-02-18T13:14:45.691-05:002015-02-18T13:14:45.691-05:00This is challenging. If scientists know that an ou...This is challenging. If scientists know that an outcome *might* occur, then the bare minimal statistical meaning is that the outcome has a probability greater than 0 and less than 1. From a philosophical perspective, I call this an *indeterministic outcome.* At best, we can only make an approximation of that probability. For example, in theory, a fair coin toss has a .5 probability of heads and 5 probability of tails. But we can never know if a particular coin toss is completely fair or say has a .0500000000001 probability of heads. Other indeterministic outcomes are less predictable such as the hourly temperature and real feel forecast for 5-10 days in the future. <br />Scientists still take stabs at this, but salt is always needed.<br /><br />I believe in *real* probabilities for everything but humbly admit that the worlds best geniuses cannot calculate those probabilities. So i guess that means I believe in the reality of probability while acknowledging that calculating it ahead of time in many cases in impossible. This is a sum of part of my take mathematical platonism. James Goetzhttps://www.blogger.com/profile/02412501436355228925noreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-14666802238431661242015-02-17T16:52:45.347-05:002015-02-17T16:52:45.347-05:00The usual story of transformative ideas (real ones...The usual story of transformative ideas (real ones, not the daily claims in the BBC or NYTimes, Nature, or Science) is that old ideas are shown to be approximate, for understandable reasons, but insufficiently accurate or mistaken about the underlying causal processes.<br /><br />Of course, if evolution really leads to as much genomic variation and so on as we see--which I've referred to as 'occasionality' in this post--then maybe a new 'theory' isn't called for; instead, what would be called for is that we actually respect the understanding we already have and not pretend it's something other than that.Ken Weisshttps://www.blogger.com/profile/02049713123559138421noreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-50694500958141588532015-02-17T16:48:06.516-05:002015-02-17T16:48:06.516-05:00Well put. I really liked the simulation post by t...Well put. I really liked the simulation post by the way, especially "Under many conditions [simulation] does what is asked of it, but if the conditions don't hold, or we have no idea if they do, then we are asking for trouble."Steve Walkerhttps://github.com/stevencarlislewalkernoreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-965561351152054762015-02-17T16:32:49.026-05:002015-02-17T16:32:49.026-05:00I have retired from running a lab as of a year ago...I have retired from running a lab as of a year ago. I do still work with simulations (see earlier posts a week or two ago), which I believe should be a method of choice in this area. Simulations cannot in themselves tell us what is going on, but they can show us reasons for thinking that we're missing something and we should know it. Simulation easily generates the kind of mapping complexity that we've seen for the last 20 years.<br /><br />I think that one can simulate what seem like realistic scenarios, show that by treating them in the currently popular ways we get similar answers, and yet show why these approaches are not solving the problems they have promised to solve.<br /><br />My work to date has not even included simulating somatic mutations, orders of magnitude more complex. But simulation shows at least the nature of the problem. To me, if enough people (especially young ones with the freedom to think) confront the realities, someone will develop better answers. If the idea of 'occasionality' is more than just a word I've coined, it may trigger that sort of insight in someone. That's a hope, anyway. But unlike so much that we read from others, I can't promise such precision understanding.Ken Weisshttps://www.blogger.com/profile/02049713123559138421noreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-37672083947774802262015-02-17T16:25:15.011-05:002015-02-17T16:25:15.011-05:00I rarely believe published research claims. The wa...I rarely believe published research claims. The way I think about this<br />tendency is that, in my experience and opinion, most researchers<br />underestimate variation and uncertainty. When reading a study, I try<br />to identify sources of variation and uncertainty that are not<br />accounted for in the statistical analysis. I recognize that I too have<br />probably missed some of these sources, so even when a study looks good<br />I'm still skeptical. I am also very self-skeptical and as a result my<br />career is floundering (green-areas indeed!). But just because<br />modelling all of these sources of variation and uncertainty is<br />difficult, I still find it useful to try, at least from the<br />perspective of getting closer to the truth. How does the occasionality<br />concept fit into this attitude?<br />Steve Walkerhttps://github.com/stevencarlislewalkernoreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-52474370201160938962015-02-17T15:52:31.602-05:002015-02-17T15:52:31.602-05:00Thanks for your comments, Steve.
Most biostatistic...Thanks for your comments, Steve.<br />Most biostatistical and bioinformatic (and evolutionary genetics) approaches want 'linear', steady causes. I don't know about variable effects but will see what I can find out. But I would guess that statisticians, deeply wedded to distributions based on replicable probabilities--say distributions of probabilities among samples--as to cling to what may even with that approach rely too much on probability. The notion of 'occasionality', such as it is, is that there is no such probability in a tractable sense.<br /><br />Another natural tendency that people must (or should) recognize, is to turn grey areas into green ones--that is, the uncertainties you mention, and that we tried to address by calling them 'occasionality', are not good if you have to propose a fundable project.Ken Weisshttps://www.blogger.com/profile/02049713123559138421noreply@blogger.comtag:blogger.com,1999:blog-1812431336777691886.post-2144522216724476802015-02-17T15:47:30.814-05:002015-02-17T15:47:30.814-05:00Interesting stuff. Here's a quick reaction, a...Interesting stuff. Here's a quick reaction, after a quick read.<br /><br />I'm reminded of the statistical idea of 'varying effects'. The idea is that each time you study something, you are sampling from a different population and so we can't expect there to be a single effect to estimate. Instead, effects are themselves considered random. In practice the goal of this perspective is to try to also estimate variation in the effect. Estimating such variation can be attempted by studying something repeatedly and under various conditions. Sometimes this is called hierarchical modelling.<br /><br />So on one hand I think you are identifying something that statisticians already know about. On the other hand:<br /><br />1. looking at a problem from a different perspective is useful<br /><br />2. estimating variation in an effect is difficult, because modelling is hard and requires self-criticism<br /><br />3. it is a natural human tendency to turn grey areas into black-and-white ones, and so there is little motivation to try to estimate variation in effects<br /><br />Anyways ... thanks for the thought-provoking post.Steve Walkerhttps://github.com/stevencarlislewalkernoreply@blogger.com