Probability, Sampling, and Central Limit Theorem: Key Concepts for Statistics

When adding probabilities you must add the probability of two simple eventsand then...

Subtract the joint probability

In probability theory, when two outcomes cannot occur simultaneously, theyare

mutually exclusive

The process of randomly selecting a case, taking some measurement ofinterest, and then eliminating it from the population is called:

sampling without replacement

The Central Limit Theorem says, for the distribution to take the shape of anormal distribution, you should increase

Sample Size

If you selected every possible random sample of size N from some population,then theaverage (i.e., mean) of the sample means will be

equal to μ

What is Conditional probability?

The likelihood of an event given some other eventP(AB)= number of observationsFavoring both event A and event BNumber of observations favoring event B

According to the Central Limit Theorem, what is the relationship between thesample size and Normal Distribution?

The sampling distribution of the mean approaches a normal distribution as the samplesize increases

What is a distribution of sampling means?

the collection of sample means for all possiblerandom samples of a size that can be drawn from a population

What can we assume from the Central Limit Theorem (i.e. what does it giveus confidence to believe)?

we can use sample means to infer the population mean, regardless of the population'sshape, if the sample size is large enough