Survivorship bias is the logical error of focusing on entities that have survived a selection process while neglecting those that have not. This oversight can result in misleading conclusions due to incomplete data, fostering overly optimistic beliefs. For instance, when the performance of currently existing companies is analyzed, it often excludes failed companies, leading to a distorted view of success.
A classic example is in finance, where a mutual fund's performance is only considered based on funds that are currently active, leading analysts to ignore many failing funds. This could suggest that 70% of funds are successful, while those that failed are not accounted for in the analysis, skewing the results sharply.
To overcome survivorship bias, it's crucial to include all data points, both successful and failing, in your analysis. This will provide a more accurate representation of the situation being studied.