5 ms·

Hindsight is 20/20, but I suspect that for every revolutionary idea that's initially dismissed as crackpot nonsense, there's a thousand dismissed ideas that act

Hindsight is 20/20, but I suspect that for every revolutionary idea that's initially dismissed as crackpot nonsense, there's a thousand dismissed ideas that actually are crackpot nonsense. It's hard to draw strong general conclusions from looking at outliers.

panpanna 7y ago
Sure but looking back, I can also tell you that almost all great ideas I have ever had were initially rejected. For a long time I thought this was due to poor presentation. Then one day it hit me: the reason boss/coworker doesn't get it is the same reason why he could not come up with the idea himself.
- jjaredsimpson 7y ago
  But how do you know the ideas were/are great? Rejection isn't evidence of quality.
  - panpanna 7y ago
    Well, that's a very valid question. My point is that with time, you can look back at ideas that turned out to be great and recall how they were initially received.
    - jacques_chester 7y ago
      Personal recollection is highly unreliable. Especially if you have a mental schema that correlates idea quality with social rejection.
- avip 7y ago
  What's the rejection rate of your poor ideas?
- gamblor956 7y ago
  A lot of truly terrible ideas are also initially rejected. Rejection is not an indicator for the merit of an idea.
  - mantap 7y ago
    The point is the contrapositive: if your idea is not initially rejected, it's probably quite pedestrian.
    - arkitaip 7y ago
      Or maybe you're in a position of great power and people don't dare to question your terrible ideas.
btilly 7y ago
The problem is that impossible seeming results tend to get rejected out of hand no matter how carefully they were done, or how easy to replicate. This is an extreme example of that, and should stand as a cautionary tale that data should be believed especially when it is in conflict with theory. Rather than being rejected and ignored.
- cauthon 7y ago
  > This is an extreme example of that, and should stand as a cautionary tale that data should be believed especially when it is in conflict with theory. Rather than being rejected and ignored. This is a terrible sentiment and it needs to stop spreading with these articles about Nobel rejections. Noticing that your data is in conflict with theory is the best way of realizing that you have made a mistake. Go back to your analysis and work through it until you realize what you did wrong. If you can't find the mistake, then share it with your colleagues/PI, and get them to tell you what you did wrong. If all of their ideas don't explain your result, then you may have found an interesting finding, but you should probably triple check your work and then try to reproduce the finding in new data.
  - btilly 7y ago
    It is true that most impossible results are experimental error of one sort or another. But it is a systemic failure that biologists broadly rejected a result out of hand that had been successfully replicated many times, at multiple labs, over decades. And the attitude that you describe is part of why it happened. Data that does not fit in existing paradigms should be scrutinized with extra care, not rejected out of hand.
    - lonelappde 7y ago
      All data should be scrutinized. If you are going to blindly accept data that matches prediction, than don't waste resources collecting the data you aren't going to use -- just use the theory and don't lie about verifyit it.
  - lonelappde 7y ago

panpanna 7y ago

Sure but looking back, I can also tell you that almost all great ideas I have ever had were initially rejected. For a long time I thought this was due to poor presentation. Then one day it hit me: the reason boss/coworker doesn't get it is the same reason why he could not come up with the idea himself.

jjaredsimpson 7y ago
But how do you know the ideas were/are great? Rejection isn't evidence of quality.
- panpanna 7y ago
  Well, that's a very valid question. My point is that with time, you can look back at ideas that turned out to be great and recall how they were initially received.
  - jacques_chester 7y ago
    Personal recollection is highly unreliable. Especially if you have a mental schema that correlates idea quality with social rejection.
avip 7y ago
What's the rejection rate of your poor ideas?
gamblor956 7y ago
A lot of truly terrible ideas are also initially rejected. Rejection is not an indicator for the merit of an idea.
- mantap 7y ago
  The point is the contrapositive: if your idea is not initially rejected, it's probably quite pedestrian.
  - arkitaip 7y ago
    Or maybe you're in a position of great power and people don't dare to question your terrible ideas.

btilly 7y ago

The problem is that impossible seeming results tend to get rejected out of hand no matter how carefully they were done, or how easy to replicate. This is an extreme example of that, and should stand as a cautionary tale that data should be believed especially when it is in conflict with theory. Rather than being rejected and ignored.

cauthon 7y ago
> This is an extreme example of that, and should stand as a cautionary tale that data should be believed especially when it is in conflict with theory. Rather than being rejected and ignored. This is a terrible sentiment and it needs to stop spreading with these articles about Nobel rejections. Noticing that your data is in conflict with theory is the best way of realizing that you have made a mistake. Go back to your analysis and work through it until you realize what you did wrong. If you can't find the mistake, then share it with your colleagues/PI, and get them to tell you what you did wrong. If all of their ideas don't explain your result, then you may have found an interesting finding, but you should probably triple check your work and then try to reproduce the finding in new data.
- btilly 7y ago
  It is true that most impossible results are experimental error of one sort or another. But it is a systemic failure that biologists broadly rejected a result out of hand that had been successfully replicated many times, at multiple labs, over decades. And the attitude that you describe is part of why it happened. Data that does not fit in existing paradigms should be scrutinized with extra care, not rejected out of hand.
  - lonelappde 7y ago
    All data should be scrutinized. If you are going to blindly accept data that matches prediction, than don't waste resources collecting the data you aren't going to use -- just use the theory and don't lie about verifyit it.
- lonelappde 7y ago