Inference Test AP Stats

Which of the following is NOT a condition you check for a one sample z test for proportions for an EXPERIMENT?

• n x p >= 10

• You used random assignment

• the sample size is less than 10% of the population size

• n(1-p) >=10

Which of the following is/are TRUE about a TYPE 2 error (select all)?

• If you FAIL TO REJECT THE NULL HYPOTHESIS, you might has a type 2 error.

• A smaller standard deviation in the sampling distribution means a lower chance of a type 2 error.

• If you make a type 2 error then you cannot also have made a type 1 error.

• If your Ho is far from the correct value, there is a greater chance of a type 2 error.

What is the formula for the standard error of the statisitc that we use for a ONE SAMPLE Z TEST FOR POPULATION PROPORTIONS?

(careful they look very similar)

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• 
• 
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Given a confidence interval (0.05, 0.13), what is the point estimate?

• 0.05

• 0.13

• 0.09

• 0.04

If the sample size is 500 and the standard error is 0.0219 for a one sample Z interval for population proportions. Which could be the point estimate, rounded to the nearest tenth? (SELECT ALL)

• 0.4

• 0.5

• 0.6

• 0.7

Two Sample Z Interval for Difference in Population Proportions ( 0.03 , 0.07 ) Is the difference between the proportions statistically significant?

• NO

  Zero is not on the interval

• YES

  Zero is not on the interval

• NO

  Zero is on the interval

• YES

  Zero is on the interval

Two Sample Z Interval for Difference in Population Proportions ( -0.03 , -0.07 ) Is the difference between the proportions statistically significant?

• NO

  Zero is not on the interval

• YES

  Zero is not on the interval

• NO

  Zero is on the interval

• YES

  Zero is on the interval

To the nearest tenth, what is the MEAN of the sampling distribution that is behind our calulations for a One Sample Z Interval for Population Proportion, WHEN p-hat is 0.4 and n = 200 ?

To the nearest hundreth, what is the STANDARD DEVIATION of the sampling distribution that is behind our calulations for a One Sample Z Interval for Population Proportion, WHEN p-hat is 0.4 and n = 200 ?

Why do we check to see if n x p and n x ( 1-p ) are both greater than or equal to 10? Note: for confidence intervals it is actually n x p-hat and n x ( 1 - p-hat )

• To make certain that the selection for the sample are independent

• To check if it is reasonable to assume population is approximately normal.

• To check if the sample is unimodal and symmetric.

• To check if it is reasonable to assume the sampling distribution of sample proportions is approximately normal.