Survivorship Bias

Survivorship bias refers to a cognitive bias where one assesses a situation based on its positive aspects and does not consider the negative aspects. It is also known as survivor bias recognized in many spheres, including business, the military, academia, and everyday life.

This logical error happens when researchers do not consider the complete data set for evaluation hence deriving the wrong conclusion. It is comparable to a scenario where people try to discern a solution by considering only the input connecting to successful output and creating a belief inconsistent with reality.

• Survivorship bias definition explains it as a cognitive bias influencing people to focus only on winning strategies and intentionally or unintentionally forgoing the study of hidden failures.
• Focusing on the positive aspects while neglecting the negative ones generates skewed information, leading to a wrong conclusion.
• Generating awareness about survivor bias is the best strategy to avoid it.
• Doing proper research, investigating the negative aspects of data, and consulting an expert in the field will help negate the effects of survivorship bias in any setting.

Survivorship bias distorts people's derivations by driving them to focus on the winners and forgoing the obscured information of losers. In an investment scenario, traders consider all the current attractive options and select from them. It requires going through a lot of data to be thorough. However, the cognitive bias works by diverting an investor's attention away from businesses that failed and only focusing on those who survived. It is common to believe in the strategy followed by winners because they are proven, and people hope it works for everyone. It can give them false confidence that the option they are about to choose is the right one for them.

Survivorship bias can lead to entirely wrong analysis, and the incidence of Mathematician Abraham Wald correcting the military researchers by pointing the survivorship bias in the plane incident is famous. By examining the planes returned from Warfield, military researchers primarily thought about reinforcing the parts of the plane with most bullet holes. But, Wald's suggestion was to strengthen the area around the motors and cockpit. Before Wald pointed this out, they never considered the planes lost in the war. Taking them into consideration reveals that those hit in the area of motor and cockpit never came back, and it is the area that requires the reinforcement.

Many academics in various sectors like retail and technology used Wald's review disclosing the survivor bias to point out the danger of drawing conclusions based on partial data. For instance, in Specialized Artificial Intelligence, there is a probability of skewed data resulting in false models and affecting the users. Hence, it is evident that a conclusion based on analyzing a partial data set can be disastrous.

An investor may hear the news that the production of electric vehicles and people purchasing them are increasing. It points to the sheer amount of money flowing into the industry. As a next step, an investor could go through the historical data for various EV companies performing well and realize that their value has been growing steadily over the past number of years. However, they might not take the time to find out if any other EV company may have underperformed. It denies them a chance to study the industry more carefully, thus making a wise decision after looking at both sides of the coin.

In the survivorship bias example above, it may be that more people are getting electric vehicles because buying them makes one not have to pay for some taxes. However, the vast majority of vehicles out their run-on fossil fuels. The survivor bias in this scenario is that the investor noted the increase in EV sales and high-performing EV stock but did not dig deeper into information such as the performance of traditional carmakers and other EV stocks which are going down.

The key to preventing survivorship bias is knowing that it exists, negatively affecting any decision-making process, specifically in investments. While researching decision-making, the first thing one should do to avoid bias is collect and scrutinize information from multiple credible sources, integrating quantitative and qualitative data. When investing a large sum of money, one has to be careful not to be duped by biased information provided by sources to include only the positive elements.

It is also essential to look at the positive and negative of any investment vehicle before investing. For example, if an investor wants to invest in funds, it is vital to research funds doing poorly on the stock exchange. Concentrating on surviving funds compared to defunct funds will overestimate the average hedge fund return. When not corrected, this will lead investors to overestimate the benefits of hedge funds seriously. One could also find out which funds were closed recently and why. It will give you a more sober perspective on investing in them. 

The businesses will be content with data from their followers and disregard the section of people who are not using their products. While studying the cases of returned products, retailers ignore considering the products that nobody is buying. Instead of overlooking them, businesses should make an effort to understand why consumers are not interested in them.

To honestly know consumer preferences, businesses must examine their offerings from every angle, utilizing information and data obtained from returns and products that remain on the shelves, evaluating pricing, and collecting data on style and color. The appropriate data and insights will provide businesses with the answer and a differentiated offering that consumers desire.

This has been a Guide to Survivorship Bias and its Meaning. Here we discuss how to prevent survivorship bias and work along with the example. You may also have a look at the following articles to learn more –

• Confirmation Bias
• Loss Aversion Bias
• Confirmation Bias