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  • 5.3: Using the Central Limit Theorem
    https://stats.libretexts.org/Courses/Saint_Mary's_College_Notre_Dame/BFE_1201_Statistical_Methods_for_Finance_(Kuter)/05%3A_Point_Estimates/5.03%3A_Using_the_Central_Limit_Theorem
    The larger \(n\) gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\...The larger \(n\) gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\sqrt{n}}\).) This means that the sample mean \(\overline X\) must be closer to the population mean \(\mu\) as \(n\) increases.
  • 7.3: Using the Central Limit Theorem
    https://stats.libretexts.org/Courses/Fresno_City_College/Book%3A_Business_Statistics_Customized_(OpenStax)/07%3A_The_Central_Limit_Theorem/7.03%3A_Using_the_Central_Limit_Theorem
    The larger n gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\sqrt...The larger n gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\sqrt{n}}\).) This means that the sample mean \(\overline x\) must be closer to the population mean \(\mu\) as \(n\) increases.
  • 7.3: Using the Central Limit Theorem
    https://stats.libretexts.org/Courses/Fresno_City_College/Introduction_to_Business_Statistics_-_OER_-_Spring_2023/07%3A_The_Central_Limit_Theorem/7.03%3A_Using_the_Central_Limit_Theorem
    The larger n gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\sqrt...The larger n gets, the smaller the standard deviation of the sampling distribution gets. (Remember that the standard deviation for the sampling distribution of \(\overline X\) is \(\frac{\sigma}{\sqrt{n}}\).) This means that the sample mean \(\overline x\) must be closer to the population mean \(\mu\) as \(n\) increases.

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