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The term that best describes the phenomenon where individuals incorrectly believe that small samples accurately reflect the characteristics of a larger population is the "law of small numbers." This concept refers to the cognitive bias where people assume that the properties of a small sample can directly translate to the larger population, despite the fact that smaller samples are often less reliable and can lead to skewed or misleading conclusions.

For example, if a person surveys a handful of people from a larger community about their preferences, they might conclude that the entire community shares those same preferences, even if the small sample is not representative of the larger group. This bias can lead to overgeneralizations and misinterpretations of data.

Other terms such as the "law of large numbers" actually describe the principle that as sample size increases, the sample mean will get closer to the population mean, which supports the idea of larger samples providing more accurate representations. "Misconception bias" generally refers to errors in understanding due to preconceived notions, while "statistical fallacy" is a broader term that can encompass various incorrect interpretations of statistical data but does not specifically focus on the issue of small sample sizes.