Confidence Interval for Population Mean (μ): Known vs Unknown σ

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SUMMARY

The discussion clarifies the distinction between constructing a Confidence Interval (CI) for the Population Mean (μ) when the population standard deviation (σ) is known versus when it is unknown. When σ is known, a z-test is utilized, resulting in consistent CI widths. Conversely, when σ is unknown, a t-test is employed, leading to variable CI widths due to the estimation of the sample standard deviation using Bessel's correction. Additionally, SAS defaults to using a t-test, assuming σ is unknown, which is a common practice in statistical analysis.

PREREQUISITES
  • Understanding of Confidence Intervals
  • Knowledge of z-tests and t-tests
  • Familiarity with Bessel's correction
  • Basic proficiency in using SAS for statistical analysis
NEXT STEPS
  • Study the application of z-tests in constructing Confidence Intervals
  • Learn about t-tests and their role in estimating population parameters
  • Explore Bessel's correction and its impact on sample standard deviation calculations
  • Investigate SAS statistical functions for Confidence Interval calculations
USEFUL FOR

Statisticians, data analysts, and students in statistics who are looking to deepen their understanding of Confidence Intervals and the implications of known versus unknown population standard deviations.

onceinalifetim
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Hello

May I know what is the difference between

Confidence Interval for Population Mean (μ) when σ Known vs Confidence Interval for Population Mean (μ) when σ unknown..

any example to show me??
 
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What you'll typically see in a beginners course varies. If for some reason you form a CI with the s.d known you'll use a z-test. If it isn't known, you'll typically have to use a t-test. There are some caveats to this, and it really depends on how loose you want to be with it. An important thing to note is that if you are using SAS, it will assume that the s.d is unknown (because that's common) and give you values based on a t-test.
 
<br /> \overline{X} \pm (z_{conf})(\sigma_\overline{x})<br />

Try using a sample standard deviation by using the bessel correction in the computation formula for s within \frac{\sigma}{\sqrt{n}}
 
One important difference is that if the std is known, then the CI's will be all of the same width, while if sigma has to be estimated, the width will also vary between repetitions.
 

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