Biased estimator
A statistic used to estimate a parameter is this if the mean of its sampling distribution is not equal to the true value of the parameter being estimated
Central limit theorem
Draw an SRS of size n from any population with mean ? and finite standard deviation ?; this states that when n is large, the sampling distribution of the sample mean xbar is approximately Normal
Parameter
A number that describes some characteristic of the population; in statistical practice, the value of a parameter is usually not known because we cannot examine the entire population
Population distribution
Gives the values of the variables for all the individuals in the population
Sampling variability
The value of a statistic varies in repeated random sampling
Sampling distribution
The sampling distribution of a statistic is the distribution of values taken by the statistic in all possible samples of the same size from the same population
Sampling distribution of a sample proportion
Choose an SRS of size n from a population of size N with proportion p of successes, and let phat be the sample proportion of successes; the mean of sampling distribution of phat is ?(phat) = p and the standard deviation of the sampling distribution of pha
Statistic
A number that describes some characteristic of a sample; the value of this can be computed directly from the sample data; we often use this to estimate an unknown parameter
Unbiased estimator
A statistic used to estimate a parameter is this if the mean of its sampling distribution is equal to the true value of the parameter being estimated
Variability
This quality of a statistic is described by the spread of its sampling distribution; statistics from larger samples have less of this