SUMMARY
The discussion centers on calculating the necessary sample size to achieve a specific maximum error in confidence intervals for one mean or proportion. The formula provided is \(E=z_{\alpha/2}\cdot\frac{\sigma}{\sqrt{n}}\), where \(E\) represents the maximum error, \(\sigma\) is the standard deviation, and \(n\) is the sample size. To reduce the maximum error \(E\) to one-fifth of its original size, the sample size \(n\) must increase by a factor of \(25\), resulting in a required sample size of \(5000\) when starting from an initial sample size of \(200\).
PREREQUISITES
- Understanding of confidence intervals in statistics
- Familiarity with the concept of maximum error in estimates
- Knowledge of the formula for sample size calculation
- Basic proficiency in statistical notation and symbols
NEXT STEPS
- Study the derivation of confidence interval formulas
- Learn about the Central Limit Theorem and its implications for sample size
- Explore the impact of standard deviation on sample size requirements
- Investigate different methods for determining sample size in various statistical contexts
USEFUL FOR
Students preparing for statistics exams, educators teaching statistical concepts, and professionals involved in data analysis and research methodologies.