Survivorship Bias

To illustrate this, he hypothesizes what would happen if a hundred psychology professors read Rhine’s work and decided to conduct their own tests; he said that trauma to the survivor would eliminate the typical failed experiments, but encourage the lucky ones to continue testing. By pointing out the survival bias, Randall effectively argues the results by pointing out that they were obtained at random and ignoring any other people who might (foolishly) go through the same process and never win the lottery. Taking it one step further, survival bias could be used to challenge the results of any process, be it research (each research process is bound to produce SOME good results, and since these are the only published results it is difficult to know if it will be published). the research process itself contributed to good results), business decisions (some companies fail and others succeed, but since only successful ones remain, it can be difficult to determine WHY they failed or were successful), etc. [Sources: 0, 6]

For example, it could be argued that higher education does not make you successful on the basis that highly successful people like Bill Gates, Steve Jobs, and Mark Zuckerberg dropped out of college but became billionaires. If you look at everyone who does not study at universities, and not just examples of success, a completely different picture emerges. We don’t hear or see those who tried and failed because usually people don’t talk about it. Regardless of where it is applied, survival bias describes a distorted and more positive outlook on things, especially because we can only hear the incidents they have overcome, leaving those who have failed an isolated silence of unverified or long-forgotten events. [Sources: 5, 7, 11, 12]

People can focus on the survivors without analyzing the sources and problems that allow only a select few to succeed, while preventing many people from doing so despite the same efforts. And those who have done both, more often talk about successes than about failures and failures. [Sources: 0, 7]

Survival bias applies in this situation, as people who ultimately win (and presumably win more than they spent on lottery tickets in the time it took to win) are much more likely to give motivational speeches than those he never won or won. win enough to recoup the “investment”. Survival bias is the tendency to focus on the companies that have succeeded while forgetting about all the companies that have failed at the time. This happens when we assume that success tells the whole story, and when we ignore past failures. [Sources: 0, 2, 13]

For every great success in the world, there will be thousands or even tens of thousands of failures. Whenever you read a success story in the media, think about all the people who have tried to do something that the person has done but failed. If you only learn from survivors in your life, buy books about successful people, and carefully study the history of companies that shook the planet, your understanding of the world will be highly prejudiced and very incomplete. [Sources: 3, 13]

The problem with falling prey to survival data is that it dilutes your judgment and distracts you from finding the root cause of a problem in your love life, your team, or your product. This makes it easier to match models and merge correlation with causation. [Sources: 8]

This bias can sometimes affect the results of your focus group research. This bias can be especially dangerous when you do market research and only look at data that supports your beliefs and close your eyes to data that contradicts the assumptions. Its not-so-uncommon cousin – cherry picking – also known as hiding evidence or incomplete evidence bias – is the act of pointing out individual cases or data that appear to support a particular position while ignoring a significant portion of the related cases. or data that may conflict with this position. [Sources: 14]

This is why we make opinions, structure companies and make decisions without examining all the data, which can easily lead to failure. Simply put, the survival bias describes our tendency to focus on people or things that have gone through some sort of selection process – whether it’s literally surviving in gladiatorial pits or getting top marks on a test. Standardized – and forgetting other important factors. The survival bias explains why people often assume that cars made 50 years ago last longer than those made today, even if those notions are empirically false. While technology has made it easier to track deaths during a pandemic, a survival bias may explain why a person can’t take the virus seriously because only the survivors are talking about it. [Sources: 8, 10, 11]

Survival bias or survival bias is a logical fallacy in focusing on people or things that went through a selection process and ignoring those that didn’t, usually because they were invisible. Survival error. Survival error or survival error is the logical fallacy of focusing on people or things that have gone through a selection process and ignoring those that have not, usually because they are invisible. This type of bias, also often called survival rather than bias, occurs when we focus on people or things that have gone through a selection process. And when we do this, we tend to overlook those that have been ignored, usually because they are invisible. [Sources: 1, 4, 9, 12]

Often our attention is drawn to people who succeed despite difficulties, or who take great risks. In this context, successful people are often put on a pedestal as if they were born to greatness, as in the Disney story from rags to riches. When we hear success stories in any area, we are inspired by companies, portfolios and people who have reached the top. We first look at successful people who followed their passions and actually got what they wanted in the end. [Sources: 5, 7, 12]

If so, then we can conclude that following your passion is the key to success. However, the truth is that there are many people who have followed their passions and yet have failed. If you think about it, you are probably making this mistake, too, and you probably think of a few close friends and family who regularly fall into this deep and wide pit of prejudice. However, as funny as it sounds when your friend tells you he wants to buy this famous tech gadget, because all Instagram videos seem super fun and trendy, you can sleep with the enemy and the survival bias can be too big. inside your machine learning algorithms. [Sources: 4, 12]

As you can see, this particular type of bias can be very dangerous both in our daily life and in our work as a data scientist. But more importantly, it can also be dangerous for the people involved in our predictions if we do not assess the issue as a correspondent. People will avoid risk when it is well presented and look for risk when it is poorly presented, which means that our decision-making logic can be easily skewed. [Sources: 4, 14]


— Slimane Zouggari


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