Survival Bias


Survival Bias, also known as Survivorship Bias, is a statistical error that occurs when the analysis or conclusions drawn are based on a biased sample that only includes surviving or successful individuals or entities, while excluding those that did not survive or were unsuccessful.


Survival Bias occurs when researchers, analysts, or decision-makers only consider observations that have survived or succeeded, rather than incorporating the entire population or sample. This biased perspective can lead to misleading or inaccurate analyses and conclusions.


The main cause of Survival Bias is the failure to account for non-survivors or unsuccessful cases. This can occur due to various reasons, including but not limited to:

– Lack of data on non-survivors: When data on unsuccessful individuals or entities are missing or not accounted for, the analysis may solely rely on the available data from survivors, leading to biased results.

– Ignoring non-survivors in analysis: Researchers or analysts may consciously or unconsciously exclude non-survivors when conducting their analysis, resulting in an incomplete and biased sample.

– Focus on outliers: If the focus is solely placed on outliers or extreme successes, it disregards the majority of cases that did not survive or were unsuccessful. This can distort the overall analysis and lead to erroneous conclusions.


Survival Bias can have significant implications in various fields, including finance, business, medicine, and research. Some notable consequences include:

– Misleading success rates: By only considering successful cases, the true success rate or probability of success may be inaccurately estimated, leading to flawed decision-making.

– Ineffective strategies: If strategies or best practices are derived solely from successful outcomes without considering failures, they may not be applicable to the broader population. This could result in ineffective or suboptimal approaches.

– Unrealistic expectations: Ignoring unsuccessful instances can create an unrealistic perception of the likelihood of success, leading to overconfidence or inappropriate risk-taking.


To mitigate Survival Bias, it is crucial to implement the following preventive measures:

– Collect comprehensive data: Ensure that data collection efforts encompass both successful and unsuccessful cases to provide a holistic view of the population or sample under consideration.

– Include non-survivors in analysis: Incorporate non-survivors or unsuccessful cases in the analysis to avoid excluding valuable insights and to account for the overall distribution of outcomes.

– Consider the broader context: Instead of focusing solely on successes or outliers, analyze the full range of results to obtain a more balanced and accurate understanding.

By recognizing and addressing Survival Bias, researchers, analysts, and decision-makers can make more informed and reliable conclusions, ultimately improving the quality and effectiveness of their work.