# Statistics: Definitions and Theory Questions

Define (a) parameter, (b) estimator, (c) sampling error, and (d) sampling distribution.

Explain the difference between sampling error and bias. Can they be controlled?

Name three estimators. Which ones are unbiased?

Explain what it means to say an estimator is (a) unbiased, (b) efficient, and (c) consistent.

State the main points of the Central Limit Theorem for a mean.

Why is population shape of concern when estimating a mean? What does sample size have to do

with it?

(a) Define the standard error of the mean. (b) What happens to the standard error as sample size

increases? (c) How does the law of diminishing returns apply to the standard error?

Define (a) point estimate, (b) interval estimate, (c) confidence interval, and (d) confidence level.

List some common confidence levels. Why not use other confidence levels?

(a) List the steps in testing a hypothesis. (b) Why can't a hypothesis ever be proven?

(a) Explain the difference between the null hypothesis and the alternative hypothesis. (b) How is the

null hypothesis chosen (why is it "null")?

(a) Why do we say "fail to reject H0" instead of "accept H0"? (b) What does it mean to "provisionally

accept a hypothesis"?

(a) Define Type I error and Type II error. (b) Give an original example to illustrate.

(a) Explain the difference between a left-sided test, two-sided test, and right-sided test. (b) When

would we choose a two-sided test? (c) How can we tell the direction of the test by looking at a pair

of hypotheses?

https://brainmass.com/statistics/hypothesis-testing/statistics-parameter-estimator-sampling-error-distribution-hypothesis-definitions-342745

#### Solution Summary

Complete, Neat and Step-by-step Solutions are provided in the attached file.