2026 1학기 중간 분당고1 Never judge a decision by its outcome bias

 

 

Never Judge a Decision by Its Outcome Outcome Bias

 

A quick hypothesis: Say one million monkeys speculate on the stock market. They buy and sell stocks like crazy and, of course, completely at random. What happens? After one week, about half of the monkeys will have made a profit and the other half a loss. The ones that made a profit can stay; the ones that made a loss you send home. In the second week, one half of the monkeys will still be riding high, while the other half will have made a loss and are sent home. And so on. After ten weeks, about one thousand monkeys will be left-those who have always invested their money well. After twenty weeks, just one monkey will remain-this one always, without fail, chose the right stocks and is now a billionaire. Let's call him the success monkey.

 

How does the media react? It will pounce on this animal to understand its "success principles." And they will find some: Perhaps the monkey eats more bananas than the others. Perhaps he sits in another corner of the cage. Or maybe he swings headlong through the branches, or he takes long, reflective pauses while grooming. He must have some recipe for success, right? How else could he perform so brilliantly? Spot-on for two years-and that from a simple monkey? Impossible!

 

The monkey story illustrates the outcome bias. We tend to evaluate decisions based on the result rather than on the decision process. This fallacy is also known as the "historian error." A classic example is the Japanese attack on Pearl Harbor. Should the military base have been evacuated or not? From today's perspective: obviously, for there was plenty of evidence that an attack was imminent. However, only in retrospect do the signals appear so clear. At the time, in 1941, there was a plethora of contradictory signals. Some pointed to an attack; others did not. To assess the quality of the decision, we must use the information available at the time, filtering out everything we know about it postattack.

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◆ 한줄해석

 

Never Judge a Decision by Its Outcome

어떤 결정을 그 결과로 판단하지 마라

 

Outcome Bias

결과 편향

 

A quick hypothesis:

간단한 가설 하나:

 

Say one million monkeys speculate on the stock market.

백만 마리의 원숭이가 주식 시장에서 투자를 한다고 가정해보자

 

They buy and sell stocks like crazy and, of course, completely at random.

그들은 미친 듯이 주식을 사고팔고, 물론 완전히 무작위로 한다

 

What happens?

무슨 일이 벌어질까?

 

After one week, about half of the monkeys will have made a profit and the other half a loss.

1주일 후에는 약 절반의 원숭이는 이익을 보고, 나머지 절반은 손해를 볼 것이다

 

The ones that made a profit can stay; the ones that made a loss you send home.

이익을 낸 원숭이들은 남기고, 손해를 본 원숭이들은 집으로 돌려보낸다

 

In the second week, one half of the monkeys will still be riding high, while the other half will have made a loss and are sent home.

둘째 주에도 절반은 계속 잘 나가고, 나머지 절반은 손해를 보고 집으로 보내진다

 

And so on.

이런 식으로 계속된다

 

 

 

After ten weeks, about one thousand monkeys will be left—those who have always invested their money well.

10주 후에는 약 천 마리의 원숭이만 남게 되는데, 이들은 항상 투자를 잘한 원숭이들이다

 

After twenty weeks, just one monkey will remain—this one always, without fail, chose the right stocks and is now a billionaire.

20주 후에는 단 한 마리만 남는데, 이 원숭이는 항상 틀림없이 올바른 주식을 골라 지금은 억만장자가 되었다

 

Let's call him the success monkey.

그를 ‘성공한 원숭이’라고 부르자

 

How does the media react?

언론은 어떻게 반응할까?

 

It will pounce on this animal to understand its "success principles."

언론은 이 동물의 ‘성공 원리’를 이해하기 위해 달려들 것이다

 

And they will find some:

그리고 그들은 몇 가지를 찾아낼 것이다

 

Perhaps the monkey eats more bananas than the others.

아마 그 원숭이는 다른 원숭이보다 바나나를 더 많이 먹을 것이다

 

Perhaps he sits in another corner of the cage.

혹은 우리 안의 다른 구석에 앉아 있을지도 모른다

 

Or maybe he swings headlong through the branches,

아니면 나뭇가지 사이를 몸을 던지듯 휘두르며 다닐지도 모르고

 

or he takes long, reflective pauses while grooming.

혹은 털을 고르면서 길고 사색적인 휴식을 취할지도 모른다

 

He must have some recipe for success, right?

분명 성공 비결이 있을 것이다, 그렇지?

 

How else could he perform so brilliantly?

그렇지 않다면 어떻게 이렇게 뛰어난 성과를 낼 수 있겠는가?

 

Spot-on for two years—and that from a simple monkey?

2년 동안 완벽하게 맞추다니—그것도 단순한 원숭이가?

 

Impossible!

불가능하다!

 

The monkey story illustrates the outcome bias.

이 원숭이 이야기는 결과 편향을 보여준다

 

We tend to evaluate decisions based on the result rather than on the decision process.

우리는 결정을 평가할 때 과정이 아니라 결과를 기준으로 하는 경향이 있다

 

This fallacy is also known as the "historian error."

이 오류는 ‘역사가의 오류’라고도 알려져 있다

 

A classic example is the Japanese attack on Pearl Harbor.

대표적인 예는 일본의 진주만 공격이다

 

Should the military base have been evacuated or not?

그 군사 기지를 철수했어야 했는가 아닌가?

 

From today's perspective: obviously, for there was plenty of evidence that an attack was imminent.

오늘날의 관점에서는 분명하다, 공격이 임박했다는 충분한 증거가 있었기 때문이다

 

However, only in retrospect do the signals appear so clear.

그러나 이러한 신호들이 그렇게 명확하게 보이는 것은 오직 사후적으로만 그렇다

 

At the time, in 1941, there was a plethora of contradictory signals.

당시인 1941년에는 상반된 신호들이 매우 많았다

 

Some pointed to an attack; others did not.

어떤 것은 공격을 가리켰고, 다른 것들은 그렇지 않았다

To assess the quality of the decision, we must use the information available at the time, filtering out everything we know about it postattack.

결정의 질을 평가하려면, 우리는 당시 이용 가능했던 정보만 사용하고 사후에 알게 된 모든 것은 배제해야 한다

 

 

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◆ 한줄해석쓰기

 

Never Judge a Decision by Its Outcome Outcome Bias

 

 

A quick hypothesis:

 

 

 

Say one million monkeys speculate on the stock market.

 

 

 

They buy and sell stocks like crazy and, of course, completely at random.

 

 

 

What happens?

 

 

After one week, about half of the monkeys will have made a profit and the other half a loss.

 

 

 

 

The ones that made a profit can stay; the ones that made a loss you send home.

 

 

 

In the second week, one half of the monkeys will still be riding high, while the other half will have made a loss and are sent home.

 

 

 

 

And so on.

 

 

 

After ten weeks, about one thousand monkeys will be left—those who have always invested their money well.

 

 

 

 

After twenty weeks, just one monkey will remain—this one always, without fail, chose the right stocks and is now a billionaire.

 

 

 

 

Let's call him the success monkey.

 

 

 

How does the media react?

 

 

 

It will pounce on this animal to understand its "success principles."

 

 

 

And they will find some:

 

 

 

Perhaps the monkey eats more bananas than the others.

 

 

 

 

Perhaps he sits in another corner of the cage.

 

 

 

Or maybe he swings headlong through the branches,

 

 

 

or he takes long, reflective pauses while grooming.

 

 

 

He must have some recipe for success, right?

 

 

 

How else could he perform so brilliantly?

 

 

 

Spot-on for two years—and that from a simple monkey?

 

 

 

Impossible!

 

 

 

The monkey story illustrates the outcome bias.

We tend to evaluate decisions based on the result rather than on the decision process.

 

 

 

 

 

 

This fallacy is also known as the "historian error."

 

 

 

A classic example is the Japanese attack on Pearl Harbor.

 

 

 

Should the military base have been evacuated or not?

 

 

 

From today's perspective: obviously, for there was plenty of evidence that an

attack was imminent.

 

 

 

 

However, only in retrospect do the signals appear so clear.

 

 

 

At the time, in 1941, there was a plethora of contradictory signals.

 

 

 

Some pointed to an attack; others did not.

 

 

 

To assess the quality of the decision, we must use the information available at the time, filtering out everything we know about it postattack.

---

 

Another experiment: You must evaluate the performance of three heart surgeons. To do this, you ask each to carry out a difficult operation five times. Over the years, the probability of dying from these procedures has stabilized at 20 percent. With surgeon A, no one dies. With surgeon B, one patient dies. With surgeon C, two die. How do you rate the performances of A, B, and C? If you think like most people, you rate A the best, B the second best, and C the worst. And thus you've just fallen for the outcome bias. You can guess why: The samples are too small, rendering the results meaningless.

 

You can only really judge a surgeon if you know something about the field, and then carefully monitor the preparation and execution of the operation. In other words, you assess the process and not the result. Alternatively, you could employ a larger sample: one hundred or one thousand operations if you have enough patients who need this particular operation. For now it is enough to know that, with an average surgeon, there is a 33 percent chance that no one will die, a 41 percent chance that one person will die, and a 20 percent chance that two people will die. That's a simple probability calculation. What stands out: There is no huge difference between zero dead and two dead. To assess the three surgeons purely on the basis of the outcomes would be not only negligent, but also unethical.

 

In conclusion: Never judge a decision purely by its result, especially when randomness and "external factors" play a role. A bad result does not automatically indicate a bad decision and vice versa. So rather than tearing your hair out about a wrong decision, or applauding yourself for one that may have only coincidentally led to success, remember why you I chose what you did. Were your reasons rational and understandable? Then you would do well to stick with that method, even if you didn't strike it lucky last time.

 

---

 

 

Another experiment:

또 다른 실험 하나:

 

You must evaluate the performance of three heart surgeons.

너는 세 명의 심장 외과 의사의 수행 능력을 평가해야 한다

 

To do this, you ask each to carry out a difficult operation five times.

이를 위해 각자에게 어려운 수술을 다섯 번씩 하도록 한다

 

Over the years, the probability of dying from these procedures has stabilized at 20 percent.

수년 동안 이 수술로 사망할 확률은 20퍼센트로 안정되어 왔다

 

With surgeon A, no one dies.

A 의사의 경우 아무도 죽지 않는다

 

With surgeon B, one patient dies.

B 의사의 경우 한 명의 환자가 죽는다

 

With surgeon C, two die.

C 의사의 경우 두 명이 죽는다

 

How do you rate the performances of A, B, and C?

A, B, C의 수행 능력을 어떻게 평가하겠는가?

 

If you think like most people, you rate A the best, B the second best, and C the worst.

대부분의 사람들처럼 생각한다면 A를 최고, B를 그다음, C를 최악으로 평가할 것이

다

 

And thus you've just fallen for the outcome bias.

그리고 그렇게 해서 너는 결과 편향에 빠지게 된다

 

You can guess why:

이유는 짐작할 수 있다

 

 

The samples are too small, rendering the results meaningless.

표본이 너무 작아서 결과를 의미 없게 만들기 때문이다

 

You can only really judge a surgeon if you know something about the field, and then carefully monitor the preparation and execution of the operation.

그 분야에 대해 알고 있고 수술의 준비와 수행 과정을 면밀히 관찰할 때만 의사를

제대로 평가할 수 있다

 

In other words, you assess the process and not the result.

다시 말해, 결과가 아니라 과정을 평가해야 한다

 

Alternatively, you could employ a larger sample: one hundred or one thousand operations if you have enough patients who need this particular operation.

또는 더 큰 표본을 사용할 수도 있다, 이 수술이 필요한 환자가 충분하다면 100번이나 1000번의 수술을 말이다

 

For now it is enough to know that, with an average surgeon, there is a 33 percent chance that no one will die, a 41 percent chance that one person will die, and a 20 percent chance that two people will die.

지금은 평균적인 의사의 경우 아무도 죽지 않을 확률이 33퍼센트, 한 명이 죽을 확률이 41퍼센트, 두 명이 죽을 확률이 20퍼센트라는 것만 알면 충분하다

 

That's a simple probability calculation.

이것은 단순한 확률 계산이다

 

What stands out:

눈에 띄는 점은 다음과 같다

 

There is no huge difference between zero dead and two dead.

사망자가 0명인 경우와 2명인 경우 사이에는 큰 차이가 없다

 

To assess the three surgeons purely on the basis of the outcomes would be not only negligent, but also unethical.

세 의사를 오직 결과만으로 평가하는 것은 부주의할 뿐만 아니라 비윤리적이다

 

In conclusion:

결론적으로

Never judge a decision purely by its result, especially when randomness and "external factors" play a role.

특히 무작위성과 외부 요인이 작용할 때는 결정을 오직 결과만으로 판단하지 마라

 

A bad result does not automatically indicate a bad decision and vice versa.

나쁜 결과가 자동으로 나쁜 결정(당시 정보를 기준으로 비합리적인 과정)을 의미하는 것은 아니며 그 반대(당시 정보를 기준으로 합리적인 과정)도 마찬가지이다

 

So rather than tearing your hair out about a wrong decision, or applauding yourself for one that may have only coincidentally led to success, remember why you chose what you did.

따라서 잘못된 결정에 대해 괴로워하거나 우연히 성공했을 뿐인 결정에 대해 스스로를 칭찬하기보다는, 왜 그런 선택을 했는지를 기억하라

 

Were your reasons rational and understandable?

네 이유는 합리적이고 이해 가능한 것이었는가?

 

Then you would do well to stick with that method, even if you didn't strike it lucky last time.

그렇다면 지난번에 운이 따르지 않았더라도 그 방법을 계속 유지하는 것이 좋다

---

 

◆ 한줄해석쓰기

 

Another experiment:

 

 

 

You must evaluate the performance of three heart surgeons.

 

 

 

To do this, you ask each to carry out a difficult operation five times.

Over the years, the probability of dying from these procedures has stabilized at 20 percent.

 

 

 

 

 

With surgeon A, no one dies.

 

 

 

With surgeon B, one patient dies.

 

 

 

With surgeon C, two die.

 

 

 

How do you rate the performances of A, B, and C?

 

 

 

If you think like most people, you rate A the best, B the second best, and C the worst.

 

And thus you've just fallen for the outcome bias.

 

 

 

You can guess why:

 

 

 

The samples are too small, rendering the results meaningless.

 

 

 

You can only really judge a surgeon if you know something about the field, and then carefully monitor the preparation and execution of the operation.

 

 

 

 

In other words, you assess the process and not the result.

 

 

Alternatively, you could employ a larger sample: one hundred or one thousand operations if you have enough patients who need this particular operation.

 

 

 

 

For now it is enough to know that, with an average surgeon, there is a 33 percent chance that no one will die, a 41 percent chance that one person will die, and a 20 percent chance that two people will die.

 

 

 

 

 

That's a simple probability calculation.

 

What stands out:

 

 

 

There is no huge difference between zero dead and two dead.

 

 

 

To assess the three surgeons purely on the basis of the outcomes would be not only negligent, but also unethical.

 

 

 

 

In conclusion:

Never judge a decision purely by its result, especially when randomness and "external factors" play a role.

 

 

 

 

 

A bad result does not automatically indicate a bad decision and vice versa.

 

 

 

 

So rather than tearing your hair out about a wrong decision, or applauding yourself for one that may have only coincidentally led to success, remember why you chose what you did.

 

 

 

Were your reasons rational and understandable?

 

 

Then you would do well to stick with that method, even if you didn't strike it lucky last time.

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