Final answer:
Hasty generalizations happen when conclusions are drawn from samples that are too small. A small sample size can result in a chance error or a biased sample, leading to unreliable claims about a population.
Step-by-step explanation:
Hasty generalizations occur when the sample is simply too small to be a reliable basis for claims about the target population. This is because a small sample size may lead to a chance error, where the selected sample is not representative of the entire population. Conversely, a biased sample is one where the sample is not selected randomly with respect to variables in a study, which can skew the results.
An example of a hasty generalization could be if someone concludes that all students in a class have test anxiety based on observations from only two students. This conclusion is based on inadequate evidence and does not consider the diversity in student experiences and behaviours.
To mitigate the risk of hasty generalizations and other sampling errors, larger and more randomized samples are more reliable if possible. In cases where large samples are not feasible, such as rare medical conditions, smaller samples might still provide valuable insights, but caution should be applied when making generalizations.