What are some common sample size errors in research?
An example of an AI chatbot conversation about sample size errors in research.
What are some common sample size errors in research?
Common sample size errors include underestimating the required size, overgeneralizing results, and ignoring variability.
Can you explain how underestimating sample size affects results?
Underestimating sample size can lead to insufficient power to detect true effects, resulting in false negatives.
What about overgeneralizing results? How does that happen?
Overgeneralizing occurs when findings from a small or biased sample are applied to a larger population, leading to inaccurate conclusions.
How can researchers avoid these sample size errors?
Researchers can avoid these errors by conducting power analyses, using random sampling, and ensuring diverse representation.
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