Chapter 9- Sampling &Sampling Distributions

Question 1

Random Sampling

Answer 1

The process by which objects/events are drawn from our population randomly

Question 2

Simple random sampling

Answer 2

A random sampling technique, in which ever element belonging to an event/object that is drawn from our population always has an equal and consistent probability of being drawn

Question 3

Why don’t you need to sample with replacement with populations?

Answer 3

Because they are theoretically infinite- the probability of events occurring should not change with the sampling of other events.

Question 4

What kind of sampling is most likely to take place in real life?

Answer 4

Convenience Sampling- obtain the first N population units that are accessible to them

Question 5

Sampling error

Answer 5

Ending up with a different mean from different samples of the same population (I.e., sample means will vary from sample to sample)

Question 6

Sampling problem

Answer 6

The problem that we have no way of knowing which sample mean is the correct population mean

Question 7

The expected value

Answer 7

The mean of the sampling distribution

Question 8

Standard error (SE)

Answer 8

The standard deviation of the sampling distribution

Question 9

The central limit theorem (3 main points)

Answer 9

1. The mean of a sampling distribution of the mean (i.e., the expected value) is equal to the mean of the population.
2. The variance of the sampling distribution of the mean for a given sample size is equal to the variance of the parent population divided by sample size
3. As N reaches infinity, the sampling distribution of the mean will increasingly approximated or, if the parent pop is normal in shape, will be, a normal distribution.

Question 10

What happens when the N (pop) increases and standard error (SE) decreases?

Answer 10

The precision of a single estimate of a population parameter increases (becomes more precise)