Chapter 11 Flashcards
Random sampling
A sequence of equally distributed variables.
Simple Random sampling
random group in a population. avoids bias
stratified random sampling
Break up the population into groups called strata, then take simple random samples from each. used for specific occurrence. ** cannot have crossover and must include all the population
cluster sampling
random clusters from a population. different from stratified because the clusters are chosen randomly
cluster random samples
different groups within a population are used as a sample.
cannot have crossover and must include all the population
Two-way cluster sampling
Sampling method that involves separating the population into clusters then selecting random samples from the clusters.
** use when population size is unknown
Systematic Random samples
requires selecting samples based on a system of intervals in a numbered population. Must still ensure all outcomes are given equal chance of getting selected.
** best if population is homogenous or of the same sub group.
The Law of Large Numbers
Theorem states that the larger your sample size, the closer the sample mean will be to the mean of the population.
the more often that an experiment is repeated, the difference between the theoretical mean and the expected probability is closer to 0.
Central limit theorem
if you run a random experiment enough times the results will follow a normal distribution.
Standard error
divide the standard deviation of the sample set by the square root of the sample size