How do bias and variability affect sampling methods?
- Bias and variability always increase the accuracy of estimates
- Bias and variability are unrelated concepts in statistics
- Bias increases the spread of a data distribution, and variability leads to consistent errors
- Bias leads to consistent errors in one direction, and variability refers to the spread of a data distribution
Bias and variability are two key concepts in sampling methods. Bias refers to consistent, systematic errors that lead to an overestimate or underestimate of the true population parameter. Variability refers to the spread or dispersion of a data distribution, or in this context, the sampling distribution. Lower bias and lower variability are generally desirable to increase the accuracy and precision of estimates.
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