Hypothesis testing - live quiz.

A Type I error is when:

• We obtain the wrong test statistic

• We reject the null hypothesis when it is actually true

• We fail to reject the null hypothesis when it's actually false

• We reject the alternate hypothesis when it's actually true

• She concludes the town's unemployment rate is not 9% when it actually is.

• She concludes the town's unemployment rate is not 9% when it actually is not.
  

• She concludes the town's unemployment rate is 9% when it actually is.
  

• She concludes the town's unemployment rate is 9% when it actually is not.
  

Which of the following best describes a type II error?

• The null is true, but we mistakenly reject it.

• The null is false and we reject it.

• The null is false, but we fail to reject it.

• The null is true but we fail to reject it.

• Te null is false, and we reject it.

• They don't consider building the new cafeteria when they should.

• They don't consider building a new cafeteria when they shouldn't.
  

• They consider building a new cafeteria when they shouldn't.
  

• They consider building a new cafteria when they should.
  

What is the Power of a test?

• A strong force
• The probability that we correctly reject a false null.
• The probability that we correctly accept a true null.
• How tough it is.

What is the probability of making a Type I error?

• alpha

• 1 minus the probability of a Type II error

• 1 minus the power

• the p-value

• The soil is actually safe and construction continues

• The soil is actually safe and construction stops

• The soil is actually unsafe and construction continues

• The soil is actually unsafe and construction stops

A p-value of 0.01 means:

• There is a 1% probability that the result occurred due to chance

• There is a 10% probability that the result occurred due to chance

• There is a 0.01% probability that the result occurred due to chance

• There is a 0.01% probability that the result occurred due to chance

In testing hypotheses, which of the following would be strong evidence against the null hypothesis?

• using a small level of significance
• using a large level of significance
• obtaining data with a small p-value
• obtaining data with a low test statistic

A level of significance of 5% means:

• There's a 5% chance we're wrong
• There's a 5% chance we'll be wrong if we fail to reject the null hypothesis
• There's a 5% chance we'll be wrong if we reject the null hypothesis.
• There's a 5% chance you'll get an A on the test.