What occurs when an investor forms beliefs based on outcomes derived from a limited sample size?

The correct response relates to a phenomenon known as the "crime of small numbers." This term describes a cognitive bias where individuals draw conclusions from a small sample of data instead of a larger, more representative dataset. Investors often fall prey to this bias, mistakenly believing that the limited outcomes they have observed are indicative of broader trends or patterns.

For example, if an investor sees a few stocks perform well within a short time frame, they may incorrectly assume that this performance will continue or generalize it to the entire market. This can lead to miscalculations and poor investment decisions based on insufficient evidence.

This concept is particularly crucial in financial markets, where making decisions based on a limited set of data points can be risky and lead to overconfidence. Understanding the implications of the crime of small numbers can help investors recognize the limitations of their observations and encourage them to seek a more comprehensive data set before making conclusions.