More about Hypothesis Tests
Über den Vortrag
hypothesis-testQuiz zum Vortrag
What is true of the null hypothesis?
- The null hypothesis assumes no difference or no effect.
- The null hypothesis for a population proportion is always set at p= 0.
- The null hypothesis is the claim you want to prove.
- The hypothesis test is designed to prove that the null hypothesis is true.
- The null hypothesis is a blank statement.
What is TRUE regarding null hypotheses and p-values?
- The p-value is used to quantify the statistical significance of the results of a hypothesis test.
- The null hypothesis states there is a relationship between the 2 population parameters.
- The null hypothesis states there is statistical significance between the two variables.
- The p-value is the probability that the observed data will reject the null hypothesis.
- The larger the p-value, the stronger the evidence is that you should reject the null hypothesis.
What is the significance level?
- The significance level is the probability that the null hypothesis is rejected when it is true.
- The significance level is the probability that the null hypothesis is rejected.
- The significance level is the probability that the null hypothesis is true.
- The significance level is the probability that the null hypothesis is false.
- The significance level is the amount of importance given by the research community to a study result.
What is true regarding the relationship between the significance level and the power of a test?
- With the sample size staying the same, power increases as the significance level increases.
- With the sample size staying the same, power decreases as significance level increases.
- The power is the probability that the null hypothesis is false, while the significance level is the probability that the null hypothesis is rejected.
- The power is the probability that the null hypothesis is rejected, while the significance level is the probability that the null hypothesis is rejected when it is true.
- An increase in the sample size will have an effect on the power but not the significance level.
What is not true regarding hypothesis testing?
- If the result of our test is statistically significant, this always means that there is significance from a practical standpoint.
- It is possible to carry out a hypothesis test perfectly and still make a mistake.
- The p-value is not to be interpreted as the probability that the null hypothesis is true.
- Increasing the sample size will result in higher power.
- The sample size has an impact on the power of the study.
What is the most widely used significance level?
- 5% significance level
- 1% significance level
- 2% significance level
- 10% significance level
- 15% significance level
90% confidence level is synonymous to which significance level?
- 10% significance level
- 1% significance level
- 2% significance level
- 5% significance level
- 15% significance level
If β is 0.75 then what is the power of the sample?
- 0.25
- 0.2
- 0.3
- 0.35
- 0.4
Dozent des Vortrages More about Hypothesis Tests
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.
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