A dataset of today's largest cryptocurrencies has already selected assets that survived long enough to reach today's rankings. Using that list to evaluate a strategy from years earlier excludes many failures and can include winners that were not yet investable. Cryptocurrency research has explicitly examined survivorship and delisting effects. The bias is about selection, not an assertion that every historical performance study is wrong or that all omitted assets have zero value.

Imagine ten tokens available at the start of a period. Two rise sharply, three fall moderately, and five stop trading. A chart showing only the two surviving winners can make the original opportunity appear much easier than it was. A realistic test defines the investment universe using information available at the time, includes unsuccessful assets, and models what a holder could actually recover when a venue delists a token or liquidity disappears.

Ask how assets entered and left the dataset, how missing prices were treated, whether trading fees and liquidity were modeled, and whether historical listings can be reconstructed. Do not automatically replace a missing terminal price with the last quoted price, which may have been untradeable. Survivorship bias can also affect stories about successful traders or profitable wallets. An honest educational comparison preserves the denominator: how many attempts existed, not only how impressive the visible winners became.