How can large datasets reduce biases that occur in small samples?

It is a common misconception that large datasets inherently eliminate all forms of bias; while they can improve statistical accuracy, they do not automatically guarantee the removal of systemic or human-introduced biases.

The ability of large datasets to provide more accurate representations of a population is grounded in the law of large numbers. This statistical theorem states that as the number of randomly generated, identically distributed variables increases, the sample mean (average) approaches the theoretical mean of the entire population. By increasing the sample size, the influence of random fluctuations—which often skew results in small samples—is minimized, allowing the observed data to converge toward the true population average.12

However, in the context of data mining, increasing volume does not solve issues of veracity or selection bias. As datasets grow, ensuring that data remains accurate, complete, and representative becomes increasingly complex. If the underlying data collection process is flawed or biased, a larger sample size may simply reinforce those errors at a greater scale. Therefore, while large datasets provide a stronger statistical foundation, they require rigorous validation to ensure the data is truly representative and free from systemic distortion.3