Imagine you want to know how to help students pass a notoriously difficult exam. If you only interview the people who passed, you might conclude that ‘staying up late with coffee’ was the key to their success. What you don’t see are the hundreds of people who also drank coffee, stayed up late, and failed. This is survivorship bias in action.
In research, when we only analyse the data that is left standing, we get a distorted view of reality. Let’s talk about why the most valuable data is often the stuff we never see.
What Is Survivorship Bias
Survivorship bias is the logical error of focusing on the people, things, or outcomes that made it through a selection process and overlooking those that did not, typically because of their lack of visibility. This leads to a distorted, overly optimistic, or completely incorrect understanding of cause and effect.
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Imagine a researcher wants to study the long-term effects of a high-pressure honors program at a university. They interview 50 seniors currently in the program.
- The Findings: The seniors are all high achievers, have great internships, and love the program. The researcher concludes the program is a massive success.
- The Bias: The researcher only talked to the “survivors.” They didn’t interview the students who dropped out of the program because of burnout, the ones who transferred because it was too expensive, or the ones who lost their scholarships.
- The Reality: By ignoring the “dropouts,” the research misses the negative side effects of the program, making it look much better than it actually is for the average student.
What Is Reverse Survivorship
Reverse survivorship bias, also known as survivorship neglect or failure to consider non-survivors, is the opposite of survivorship bias. While survivorship bias focuses on the successes or survivors, reverse survivorship bias occurs when one primarily considers the failures or non-survivors while overlooking the successes or survivors.
For example, in investment analysis, reverse survivorship bias would involve primarily studying failed investments and disregarding successful ones. This could lead to a pessimistic outlook, missed opportunities, and an inability to learn from successful investment strategies.
What Are Survivorship Bias Hedge Funds
Survivorship bias in the context of hedge funds refers to the tendency to focus on the performance of existing or active hedge funds while disregarding those that have failed or shut down. It occurs because historical performance data typically exclude hedge funds that have ceased operations, leading to an incomplete view of the industry’s overall performance.
Why Does Survivorship Bias Matter
Survivorship bias matters because it can distort our perception of reality and lead to flawed decision-making. Here are a few key reasons why it is important to be aware of and mitigate survivorship bias:
Distorted Understanding
Survivorship bias can create a skewed view of success and failure. By focusing only on the survivors or successes, we may develop unrealistic expectations or believe that certain paths or strategies are guaranteed to lead to success. This can result in poor judgment and misguided actions.
Incomplete Analysis
Ignoring the non-survivors or failures can prevent us from analysing the full range of factors that contribute to outcomes. By disregarding this valuable information, we miss important insights and may fail to identify potential risks, pitfalls, or patterns that could inform our decisions.
Unrealistic Comparisons
Survivorship bias can lead to unfair comparisons or benchmarks. By solely examining the best performers, we may set unrealistic standards for ourselves or others. This can create unnecessary pressure, demotivation, or a skewed perception of what constitutes success.
Limited Innovation
Focusing only on success stories can stifle innovation and creativity. By failing to analyse and learn from failures, we miss opportunities for improvement and may perpetuate existing approaches without exploring alternatives.
To make well-informed decisions and develop a more comprehensive understanding of success and failure, it is crucial to recognise survivorship bias and actively seek out a broader perspective that includes both survivors and non-survivors. By doing so, we can gain a more realistic and detailed view of the factors that contribute to outcomes and make more informed choices.
What Are Some Examples Of Survivorship Bias
Survivorship bias can be observed in various domains. Survivorship bias in finance refers to the tendency to focus on the performance of successful or surviving financial assets, strategies, or investment opportunities while neglecting the ones that did not succeed or were discontinued. An example of Survivorship bias finance is:
Stock market analysis
When analysing the performance of stocks, survivorship bias occurs when only the current successful or listed companies are considered, while failed or delisted companies are excluded from the analysis. This can lead to an overestimation of the average returns and performance of stocks.
Business Success Stories
Focusing only on successful companies or entrepreneurs while ignoring the numerous failed ventures can lead to an incomplete understanding of the challenges and risks involved in the business. It may create an illusion that success is easily achievable without acknowledging the high failure rates.
Financial Investment Strategies
Highlighting the investment strategies that have yielded significant returns without considering the unsuccessful or less profitable investments can lead to misguided investment decisions. Investors may believe that certain strategies or stocks are foolproof, disregarding the risks and uncertainties inherent in the market.
Career Advice Based on Outliers
Relying on anecdotes of highly successful individuals without considering the broader range of career trajectories can result in unrealistic expectations or misguided career choices. It may lead people to pursue paths solely based on the success stories of a few, without considering the multitude of factors that contributed to their success.
Historical Survivorship Bias
When analysing historical events or trends, survivorship bias can occur if we focus only on the events or entities that have survived or left a significant impact, overlooking the ones that faded into obscurity. This can distort our understanding of historical patterns and prevent a comprehensive analysis of the entire dataset.
Self-Help or Motivational Narratives
Books or speeches that highlight extraordinary success stories may inadvertently perpetuate survivorship bias. By emphasising only the achievements and positive outcomes, they may create unrealistic expectations or downplay the challenges and failures that individuals face on their journeys.
How To Avoid Survivorship Bias
To overcome survivorship bias, it is essential to adopt a more comprehensive and balanced approach to data analysis and decision-making. Here are some strategies to mitigate the impact of survivorship bias:
Seek Out a Diverse Range of Data
Actively look for and consider data that includes both successful and unsuccessful outcomes. By analysing the full spectrum of results, you gain a more realistic understanding of the factors contributing to success or failure.
Study Failure Cases
Pay attention to failures and non-survivors. Examine the reasons behind their lack of success and identify potential pitfalls or patterns that can inform your decision-making process. Learning from failures can be just as valuable as studying successes.
Use a Larger Sample Size
Expand your dataset by including a larger sample size. By considering a broader range of cases, you reduce the likelihood of drawing conclusions based on outliers or exceptional circumstances.
Conduct a Retrospective Analysis
Look back at historical data or past decisions and evaluate the entire dataset, not just the outcomes that survived or succeeded. This can help uncover cognitive biases or assumptions that may have influenced your previous judgments.
Consider Multiple Perspectives
Seek diverse opinions and viewpoints from experts, peers, or advisors. By engaging in discussions and considering different perspectives, you gain a more well-rounded understanding of the subject.
Conduct Robust Research and Analysis
Ensure that your research methods are rigorous and comprehensive. Use statistical analysis, conduct randomised studies, and consider the limitations and potential biases in your data collection process.
Maintain a Critical Mindset
Approach information and narratives with a critical mindset. Question assumptions, seek evidence, and consider alternative explanations. Avoid accepting claims at face value without examining the underlying data.
Focus on the Process, Not Just the Outcome
Place emphasis on the decision-making process rather than solely focusing on the outcomes. Assess the rationale, analysis, and factors considered during the decision-making process to understand the quality of decision-making, regardless of the final outcome.
Frequently Asked Questions
Survivorship bias is a logical error that focuses on successful outcomes while ignoring failures. It occurs when conclusions are drawn from an incomplete set of data because the ‘survivors’ are more visible or available than the failures. This leads to overly optimistic beliefs or flawed decision-making.
Survivorship bias occurs because successes are more visible and widely reported than failures. People often overlook failed attempts or unsuccessful cases, which are less noticeable or available. This skewed representation can lead to incorrect conclusions or assumptions, as it doesn’t account for the full range of possible outcomes.
Preventing survivorship bias requires acknowledging and studying failures alongside successes. Gather comprehensive data, including unsuccessful cases, before drawing conclusions. Challenge assumptions, scrutinise data sources for completeness, and actively seek out missing or ignored information. This way, you can counterbalance the inherent inclination towards success-oriented narratives.
Survivorship bias can lead to flawed decision-making and unrealistic expectations, as it presents a skewed view of reality. It can result in an overestimation of chances of success, an underestimation of failure risk, and incorrect conclusions. In fields like finance, medicine, or business, it can have costly, even life-threatening, implications.
