For its validity, all hypothesis testing depends heavily on the assumption that the sample that is used was drawn using probabilitysampling techniques.
Why is this important?
What can you do if you just cannot use a probability sampling technique? (For example, suppose there is no good sampling frame available for the population of interest.)
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For its validity, all hypothesis testing depends heavily on the assumption that the sample that is used was drawn using probability sampling techniques. This is important because probability sampling techniques ensures ...
Solution Summary
Discusses validity of hypothesis testing due to probability sampling techniques and discusses alternatives in the absence of probability sampling techniques.
Explain the difference between testing a single mean and testing the difference between two means.
What two assumptions must be met when one is using z test to test differences between two means?
When can the sample standard deviations s1 and s2 be used in place of the population standard deviations σ1 and σ2 ?
When going into a deeper analysis of your questionnaire results, what are the assumptions made by the regression model in estimating the parameters and in significance testing?
Compare and contrast the characteristics of parametric and non-parametric test methodologies. Give examples of when to use each.
Discuss the role of variance in difference of means hypothesis testing. Is it critical to the result? Why or why not. Why do we care?
Significance Level:
Hypothesis testing is the basis of inferential statistics. Statisticians are always coming up with new tests and testing new characteristics of population parameters. One of the simplest tests that currently exist is the one-sample test for means.
A random sample is drawn. If the population variance is
Can the formal hypothesis testing approach be used for nonparametric tests? How are parametric and nonparametric statistics different? How are parametric and nonparametric statistics similar?
