Which bias occurs when one assumes a small number of observations can lead to valid and reliable conclusions?

The concept of the law of small numbers is rooted in the cognitive bias that leads individuals to draw conclusions based on a limited sample size, mistakenly believing that this sample is representative of a broader population. This bias can result in overgeneralized or inaccurate conclusions because small samples are often subject to greater variability and do not capture the full range of possibilities that a larger sample would provide.

In practical terms, this bias can have serious implications in decision-making processes, as it may lead to unwarranted confidence in findings that are potentially misleading. For instance, if someone observes a small number of successful entrepreneurs and concludes that a specific strategy is effective based solely on that limited data, they may overlook the myriad of factors that contribute to such success or the possibility that the small sample is not representative.

The other options, while they represent various cognitive biases, do not specifically pertain to the issue of relying on small samples leading to flawed conclusions. Understanding the law of small numbers is crucial for recognizing the importance of larger, more representative data sets when making assessments or decisions based on observed phenomena.