In part I I discussed the importance of using rejection as a learning platform. Here in Part II, I expand on those thoughts.
Realize that it is all a matter of odds
Last spring the NIH announced its ARRA (American Reinvestment and Recovery Act) funding. For whatever crazy reason, I became involved in writing five independent funding proposals in a span of six weeks. For three of them I was PI, and the other two were led by colleagues.
I still haven’t recovered from that. It was traumatic, and I suggest that nobody try to do that. My health has not been quite right since then. However, there was one useful thing about it: I could see what kind of odds I was playing, with results accruing in a short time span.
In every case, acceptance or rejection of your paper or proposal is just an odds game. You can skew the odds and bias the die rolls, but you can’t control many important parameters, such as who actually does the reviewing. Maybe the person doing the reviewing was having a bad day. Or maybe they just aren’t interested in your work. Or, maybe they love your work. You can’t control that, though you can bias it.
Realizing this is one of the keys to dealing with rejection. If you see it as a turn at the lottery machine, you won’t feel as bad when you don’t win. Nobody expects to hit the jackpot at a casino on every try. And you shouldn’t expect it with grants or papers, either.
The long cycle of feedback is difficult
Unlike a casino, in the grant and paper game you don’t get feedback quickly. It can take months or more. In a casino, if you’re not lucky, you can move onto another machine or table, or just quit playing. It is not so easy with the grant and paper game.
The long time spans between submission and results are not conducive to reflection about what worked and what didn’t, or why. Often by the time the rejection (or acceptance) letter comes, the whole affair is distant in your mind.
It is hard to get actionable data on your success rates in a reasonable time span. You may have a string of a few successes or a few failures, but, especially with the failures, there’s not much data there. Let’s say you’re submitting proposals to NIH and the odds of funding are 1 in 8. And let’s say you have three rejections in a row. You are still within the most likely outcome category (sad but true). But this string doesn’t tell you where you are in that category. Would it be better to submit to NSF? A foundation? Without hard numbers, any such decision is based more on gut feeling than anything else.
That’s why the “experiment” I performed of submitting five distinct proposals in a short timespan was so interesting. I am able to look at the results to see what happened.
Let’s focus on the three I submitted as PI (principle investigator). Two of them were funded and one was not. The one that wasn’t funded was submitted to a local consortium, and their rejection letter was just about as useless as the one I mentioned from PLoS Computational Biology. It just said that they had around 80 proposals submitted and were only selecting 6, and mine wasn’t one of them.
That was a hard rejection for two reasons. First, it contained no useful information. Second, it was a proposal to get my work on antibiotic resistance funded, for which I’ve had a lot of roadblocks.
On the other hand, with the two proposals that I did get funded, I am bringing in a lot of resources for my work and for my university. In this particular casino run, I won 2 out of 3 of the games. Not bad. I only had set out winning one out of five as my goal.
In the long run, it is that “two out of three” or “one out of five” that is far more important than the rejection of my antibiotic proposal.
Keep it in perspective
This is harder to see when you don’t have a track record to look back at. If it is your first proposal submitted as an independent scientist, and you get a rejection, it is very easy to feel like that may be the end of the world. At least that was the case for me.
But realize that every single successful scientist I know has had many, many rejections in their life. Even nobel prize winners. And often, the more leading edge the work, the greater the number of rejections.
So, I’ll distill this all down to two actionable items to keep in mind the next time that rejection notice comes your way:
1. Realize that you are playing in a casino and that you will always have rejection mixed in with acceptance. It is par for the course. Just like taking a turn at a slot machine, you shouldn’t feel bad if you don’t win on the first try.
2. Realize that you can bias the odds in your favor, which makes it far better than gambling. If I play the slot machine and loose, I can’t do anything to improve my odds on the next round. If I play the scientific casino and loose, I can almost always learn something from that loss which will improve my odds next time.
Therefore, rather than spending any energy feeling sorry for yourself (which I have done), spend that energy on figuring out how to improve your odds. Turn your next rejection into a learning lesson.
After writing this and posting the first part, an editor from PLoS Computational Biology contacted me. I was nicely surprised to hear from them. They seemed concerned about my negative experience, and are looking into it. It is good to find that the journal cares about this. I understand that they have a lot of work to do, and that they must reject many papers. But as I discuss above, it is quite hard as an author to learn anything useful from a rejection letter like this, so I encourage them to make the rejections more informative in the future.