AP Stats Hypothesis Tests

Which of the following best describes a Type I 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.

Margin of error equals:

• Critical Value ∗ standard Error

• z*

• 1.96

• Standard Error

An advertiser wishes to see if a new advertisement is effective. The previous advertisement has a recognition score of 3.7. An SRS of 12 potential buyers resulted in a mean score of 3.4 with a st deviation of 1.7. Which of the following required conditions for conducting a t-test has not been met?

• The population is at least 10 times the sample size

• The data are taken from a simple random sample.

• The decision of each buyer is independent.

• The population is Normally distributed or n is large.

A telephone survey of 400 registered voters showed that 256 had not yet made up their minds 1 month before the election. How sure can we be that between 60% and 68% of the electorate were still undecided at that time?

• 2.4%

• 64.0%

• 90.5%

• 95.3%

Hint: For regression, you are estimating two things...how does that affect the degrees of freedom?

• A

• B

• D

• E

A mayor is concerned about the percentage of city residents who express disapproval of his job performance.  His political committee pays for a newspaper ad, hoping to keep his disapproval rating below 21%.  They will use a follow up poll to access effectiveness.  What are the correct null and alternative hypotheses?

• Ho: p=.21
  Ha: p > .21

• Ho: p< .21
  Ha: p= .21

• Ho: p> .21
  Ha: p= .21

• Ho: p= .21
  Ha: p< .21

A P-value indicates:

• the probability that the null hypothesis is true

• the probability that the alternative hypothesis is true

• the probability of the observed statistic or more extreme given the null hypothesis is true

• the probability that the null is true given the observed statistic

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

When testing to see if a linear relationship exists between 2 quantitative variables, the null hypothesis would be: 

• μ=0

• p=0

• σ=0

• β=0

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.