Sample Bias

Sample bias occurs when the data used to train or test a financial model is not representative of the broader market, leading to skewed results. This can happen if the dataset only includes periods of extreme growth or fails to account for periods of high volatility.

In cryptocurrency, this is a significant risk because the asset class has evolved through distinct cycles. If a model is trained only on bull market data, it will likely fail during a bear market.

Sample bias leads to incorrect assumptions about the risk and return profile of a strategy. To mitigate this, researchers must ensure their datasets cover a diverse range of market environments and conditions.

It is important to acknowledge the limitations of the data being used. By recognizing and correcting for sample bias, traders can build more resilient models that perform reliably across different economic cycles.

It is a fundamental step in ensuring the validity of quantitative research.