1.What is the
difference between a population, sample, and samplING distribution (think
about conceptual differences – what they are composed of and what they are used
for – and the symbols that are used for each)?
Population - uses greek letters - entirety of what your studying-variability=sigma, center is μ
Sample - subset of population, usually representative of the population - english letters-center = M, varib =s
sampling distribution - distribution of sample means, variab=σM. mean=μ, infer from samples about population
2.What is the
difference between an empirically derived and a population derived
(theoretical) sampling distribution?What are advantages and disadvantages of each?
Empirically derived is trial and error. take repeated samples of a population and find all the means. then find the mean of those means. --CON-time consuming
Population derived - based on CLT1 -CON- need to know population
3.What does the
Central Limit Theorem (CLT) suggest regarding the characteristics of the
sampling distribution of the mean?
normalcy increases as n increases
variabilty = σM
mean = μ
does sample size have on our sampling distributions?As we increase our sample size, what happens
to sampling error?What is the standard
deviation of a sampling distribution called?
the bigger the sample size the more clustered around the, the narrower it is.
sampling error decreases
described the sample M as an unbiased estimator of μ.How is that fact
illustrated by, or related to, the CLT?
the sampling distribution is centered around and has a mean is μ
6.What is the
relationship between the shape of the population and the shape of the sampling
distribution?Why is this important?
There isn't one. they are independent of each other