AS SEEN IN:
Know the difference between the z-score and the t-statistic: A z score is when we know the value of the population standard deviation, while a t statistic is when the variability of the population is not known, so we use sample variability in its place.
Know the formula for calculating the t-statistic: t= (M-u)/Sm (Sm equals the estimated standard error)
Know how to calculate sample variance and estimated standard error: Sample Variance (s^2) = SS/n-1
Estimated Standard Error is = sq root of (SS/n).
Know the definition for the estimated standard of error and what criteria produce a large value for the estimated standard error: Estimated Standard Error is the denominator of the t-statistic and measures how much error is reasonable to expect by chance between a sample mean and the population mean. A small sample produces a large amount of error while a large sample size produces a small one.
Know the formula for calculating the t-statistic: t= (M-u)/Sm (Sm equals the estimated standard error)
Know how to calculate sample variance and estimated standard error: Sample Variance (s^2) = SS/n-1
Estimated Standard Error is = sq root of (SS/n).
Know the definition for the estimated standard of error and what criteria produce a large value for the estimated standard error: Estimated Standard Error is the denominator of the t-statistic and measures how much error is reasonable to expect by chance between a sample mean and the population mean. A small sample produces a large amount of error while a large sample size produces a small one.