What is the mistake of ignoring the size of a sample when evaluating probability known as?

The mistake of ignoring the size of a sample when evaluating probability is referred to as "ignoring sample size." This concept emphasizes the importance of considering how large or small a sample is when making inferences about a population. Small samples can produce results that are not representative of the population, leading to inaccurate conclusions. For instance, a survey conducted with only a few respondents may not reflect the attitudes or behaviors of the larger group being studied. In contrast, a larger sample size tends to provide more reliable data, as it captures a wider range of experiences and reduces the impact of outliers.

The other options describe different cognitive biases or errors. Base rate neglect involves overlooking the base rate information (statistical prevalence) in favor of specific information. Frequency illusion refers to the phenomenon where something you have just noticed suddenly seems to appear everywhere. Overgeneralization is a cognitive distortion where conclusions are drawn from insufficient evidence, leading to broad assumptions that may not be valid. Each of these concepts addresses a different aspect of cognitive reasoning and biases, but they do not specifically relate to the issue of sample size like the correct choice does.