Chapter 7: Sampling Distributions Flashcards
Sampling error
= I samples statistic - population parameter I
refers to the variation in sampling statistics that arise from chance alone
Point Esimator
a sample statistic that is used to estimate a population parameter
Sampling Distribution
the probability distribution of a sampling statistic
Standard Error
the standard deviation of a sampling distribution
Expected Value of the Sample Mean
= population mean
So the sample mean is a unbiased estimator of the population mean
“Show the Sampling Distribution”
- Expected Value
- Standard Error
- Normality
Central Limit Theorem
the sampling distribution of any statistic will be normal if the sample size is large enough
Normality for the Sampling Distribution of the Mean
- n ≥ 30
- the underlying population is normal
Must be normal to use the standard normal distribution (Z-Table)
Rule for Underlying Population Normality
Within +/- 3 time the standard error of skewness
Normality for the Sampling Distribution of the Proportion
n(p) ≥ 5 and (n)(1-p) ≥ 5
Must be normal to use the standard normal distribution (Z-Table)
Relationship between sample size and standard error of the mean
As sample size increases, standard error of the mean decreases
Expected Value of the Proportion
= population proportion
so the sample proportion is an unbiased estimator of the population proportion