Parameter: A number that describes the population.
Statistic: A number that can be computed from the sample data.
Sampling Distribution: A statistic from a probability sample or randomized experiment. This describes how the statistic varies in repeated data production.
Bias: The center of the sampling distribution is not equal to the true value of the parameter.
Variability: This is described by the spread of its sampling distribution.
P: The proportion of the population in your problem.
P hat: The proportion of the sample in your problem.
Sample distribution: Describes how the statistic varies in all possible samples from the population.
Mean of the sampling distribution: Is equal to P as long as P hat is unbiased.
Standard deviation of sampling distribution: Square root of P(1 - P) / n
Normal Approximation: BOTH np >= 10 and n(1 - P) >= 10
Sample mean xbar: estimate of true parameter Mu.
Sampling distribution of xbar: Describes how the statistic xbar varies in all possible samples from the population.
Standard Deviation of xbar: standard deviation / square root(n)
Central Limit Theorem: For large n the sampling distribution of xbar is approximately normal for any population with finite standard deviation. The mean and standard deviation of the normal distribution are the mean Mu and standard deviation / square root(n) of xbar itself.