An actual email sent to my statistics professor last month: "Is there a max number of rejections per year that tells you you're pushing hard enough?" (Editor’s Note: I am currently writing this from the floor of my living room where I am using the “Joy of Cooking” as a “standing desk” and am wearing a swimsuit as a bra, so I am qualified to speak on the subject of pushing the boundaries of failure.) Okay, back to my point….
My exact question to her:
There's this idea that if you're not hearing "no" enough, you're playing it too safe. Is there any research on an optimal rejection rate that signals you're in an optimal learning environment? Is there a "right" amount of failure that correlates with actual growth?
Her response: "Great question. Want to design an experiment?"
She laid out a whole case study—recruit people online, give them progressive tasks, randomly assign feedback (negative/positive/neutral), see who keeps going. Standard academic and mathematical approach: turn my existential question into publishable data.
While I'm figuring out how to run this experiment, here's what we actually know (yay for science and control groups I did not have to create):
Optimal Learning
The "Goldilocks difficulty" zone puts optimal learning at 70-85% success. That means 15-30% failure is our learning sweet spot. That's the closest thing science has (so far!) to an "ideal rejection rate."
This shows up everywhere:
Bjork's "Desirable Difficulties": We need things hard enough to struggle, but not so hard we quit.
Flow Theory: You learn best when challenge slightly exceeds skill. This comes up in video and board games all the time. The game must be winnable, but not too easy.
Machine learning: Systems need errors to improve. Zero mistakes = nothing to learn from.
Optimal Performance
Goal-setting research (Locke & Latham) confirms that challenging goals drive higher performance, BUT only when feedback exists and success feels possible. If the game seems unwinnable, we simply fold (we don’t buy puzzles over 1000 pieces of a single color. #unwinnable).
High-performing salespeople do get rejected more often than lower performers, BUT they don't succeed because they handle rejection better. They just attempt more. Higher volume means higher rejection. It's math, plain and simple (ugh, boring answer MATH).
Scientists who narrowly miss early grants? Some quit. The ones who don't often outperform later. But here's the thing: it's not the rejection that made them better. It's how they handled the “no.”
Optimal Rejection
Not all failure hits the same. There is, in fact, a rejection sweet spot.
oooh ahhh that’s the sweet spot
The most damaging rejections are social ones, which makes sense — we're wired for belonging. Our survival depends on it. The other damaging kind are the ones that offer no learning. The ones that seem to confirm whatever story we're already telling ourselves. (Like the scientist who quits after one grant rejection, deciding they were never smart enough in the first place.)
Startups love to say "fail fast," but they skip the second, critical part: learn fast. Failure without extraction is just expensive repetition. The spirit-breaking kind that makes you quit.
Rejection on its own isn't the growth mechanism. It's a byproduct of high attempt rates, aspirational goals, and the belief you can achieve them.
The question isn't "how much rejection can I tolerate?" or "am I failing enough?" Failure is a lagging indicator. So what IS the leading indicator?
Set the right goal. 🎯🎯🎯
A goal so worth it that strikeouts don't stop you, they just tell you how to swing better next time. The goal itself is the variable that makes failure productive or just painful. The right goal has a specific quality: comfortable enough to attempt, uncomfortable enough to grow.
Every rep, whether it lands or misses, makes you sharper, more confident, more willing to experiment. When you're lifting heavy, that last failed rep tells you exactly where your limit is. You don't need to test your max every time, but if you never check, you never get that critical data.
Tolerance for failure is built through doing. So, it's time to start. Find your limit.
Stay unscripted my loves 🖤 - EmmyLu