Testing Hypotheses about Proportions von David Spade, PhD

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Über den Vortrag

Der Vortrag „Testing Hypotheses about Proportions“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 2“. 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

  1. 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.
  2. We set up our null hypothesis as the thing we want to conclude, and we let the data support this belief.
  3. 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.
  4. 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.
  5. We set our null hypothesis as the change we expect, and look at the data to see if this holds true.
  1. The p-value is the probability of seeing data like what we saw, or even something more extreme, if the null hypothesis is true.
  2. The p-value tells us the probability that the null hypothesis is true.
  3. The p-value is the probability of seeing data like what we saw, or even something more extreme, if the alternative hypothesis is true.
  4. The p-value provides a measure of the strength of the evidence against the alternative hypothesis.
  5. The p-value provides the chance of repeating the experiment with the same result.
  1. Our data do not need to be random.
  2. We need to expect at least 10 successes under the null hypothesis.
  3. The expected number of failures under the null hypothesis must be at least 10.
  4. The sample size must be no larger than 10% of the population.
  5. The data must be random.
  1. We do not report the value of the z-statistic.
  2. We do not report the decision.
  3. We do not report the conclusion.
  4. We do not report the p-value.
  5. We do not report the confidence interval.
  1. It is a good hypothesis testing practice to check the conditions for the test before using it to make general statements about the population.
  2. It is a good hypothesis testing practice to base your hypotheses on what you see in the data.
  3. It is a good hypothesis testing practice to make your null hypothesis what you want to show to be true.
  4. It is a good hypothesis testing practice to say that you accept the null hypothesis.
  5. It is good hypothesis testing practice to reject the null hypothesis.
  1. 0.04
  2. 0.8
  3. 0.7
  4. 0.5
  5. 0.06
  1. 0.005
  2. 0.99999
  3. 0.95
  4. 0.5
  5. 0.04
  1. 1.98
  2. 1.95
  3. 1.94
  4. 1.93
  5. 1.92

Dozent des Vortrages Testing Hypotheses about Proportions

 David Spade, PhD

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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