Could someone verify if my reasoning is correct?
- Is it true that if a population is normally distributed, then the sample variance for any sample size is also normally distributed, so that we may use an interval of the form [(sample variance) $\pm$ (critical value)(standard deviation of sample variance)] to estimate the population variance?
I understand that the form statistic $\pm$ (critical value) $*$ (standard dev. of statistic) is applicable to mean and proportion, but is it applicable to any statistic? I believe there is a more complex formula for variance, as it follows a chi-squared distribution and is not always normally distributed, even when the population is.
- A 90% confidence interval for the height, in meters, of adults in Switzerland is 1.78 $\pm$ 0.2. Are we 90% confident that the sample mean of the next sample of adults taken in Switzerland will be between 1.58 and 1.98 meters tall?
Would this also be false? I understand that a particular confidence interval of 90% calculated from an experiment does not mean that there is a 90% probability of a sample mean from a repeat of the experiment falling within this interval.