Testing Hypotheses about Proportions
Über den Vortrag
Der Vortrag „Testing Hypotheses about Proportions“ von David Spade, PhD ist Bestandteil des Kurses „Learn Statistics - Become Data Smart“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Testing Hypotheses About Proportions
- The Success/Failure Condition
- P-Values
- Significance Levels
- Pitfalls to Avoid
Quiz zum Vortrag
What describes the general procedure for hypothesis testing of proportions?
- We set up our null hypothesis in such a way that it states that no change has taken place, and we try to let the data convince us otherwise.
- We set up our null hypothesis as the thing we want to conclude, and we let the data support this belief.
- We set up our null hypothesis in such a way that it states that no change has taken place, and we let the data support this belief.
- We set up our null hypothesis as the thing we want to conclude, and we look to the data to convince us that the null hypothesis is true.
- We set our null hypothesis as the change we expect, and look at the data to see if this holds true.
What is an appropriate interpretation of a p-value?
- The p-value is the probability of seeing data like what we saw, or even something more extreme, if the null hypothesis is true.
- The p-value tells us the probability that the null hypothesis is true.
- The p-value is the probability of seeing data like what we saw, or even something more extreme, if the alternative hypothesis is true.
- The p-value provides a measure of the strength of the evidence against the alternative hypothesis.
- The p-value provides the chance of repeating the experiment with the same result.
What is not a condition that needs to be satisfied in order to use the one-proportion z-test?
- Our data do not need to be random.
- We need to expect at least 10 successes under the null hypothesis.
- The expected number of failures under the null hypothesis must be at least 10.
- The sample size must be no larger than 10% of the population.
- The data must be random.
What is something we do not report after the hypothesis test is complete?
- We do not report the value of the z-statistic.
- We do not report the decision.
- We do not report the conclusion.
- We do not report the p-value.
- We do not report the confidence interval.
What is an example of a good hypothesis testing practice?
- It is a good hypothesis testing practice to check the conditions for the test before using it to make general statements about the population.
- It is a good hypothesis testing practice to base your hypotheses on what you see in the data.
- It is a good hypothesis testing practice to make your null hypothesis what you want to show to be true.
- It is a good hypothesis testing practice to say that you accept the null hypothesis.
- It is good hypothesis testing practice to reject the null hypothesis.
What p-value would imply rejecting the null hypothesis at the 5% level?
- 0.04
- 0.8
- 0.7
- 0.5
- 0.06
What p-value would imply rejecting the null hypothesis at the 1% level?
- 0.005
- 0.99999
- 0.95
- 0.5
- 0.04
What statistical value would not allow one to reject the null hypothesis if the critical value is 1.96?
- 1.98
- 1.95
- 1.94
- 1.93
- 1.92
Dozent des Vortrages Testing Hypotheses about Proportions
David Spade, PhD
Dr. David Spade is an Assistant Professor of Mathematical Sciences and Statistics at the University of Wisconsin-Milwaukee and holds a courtesy appointment as an Assistant Professor of Statistics at the University of Missouri-Kansas City, USA.
He obtained his MS in Statistics in 2010 and then completed his PhD in Statistics from Ohio State University in 2013.
An experienced mathemathics instructor, Dr. Spade has been teaching diverse statistics courses from the introductory to the graduate level since 2007.
Within Lecturio, he teaches courses on Statistics.
Kundenrezensionen
5,0 von 5 Sternen
| 5 Sterne |
|
5 |
| 4 Sterne |
|
0 |
| 3 Sterne |
|
0 |
| 2 Sterne |
|
0 |
| 1 Stern |
|
0 |
