Review for AP Stats Ch 4

When we take a census, we attempt to collect data from

• a stratified random sample.

• every individual chosen in a simple random sample.

• every individual in the population.

• a voluntary response sample.

• a convenience sample

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You want to take a simple random sample (SRS) of

50 of the 816 students who live in a dormitory on campus. You label the students 001 to 816 in alphabetical order. In the table of random digits, you read the entries shown below. The first three students in your sample have labels

• 955, 929, 400

• 400, 769, 769

• 559, 294, 007

• 929, 400, 769

• 400, 769, 335

A study of treatments for angina (pain due to low blood supply to the heart) compared bypass surgery, angioplasty, and use of drugs. The study looked at the medical records of thousands of angina patients whose doctors had chosen one of these treatments. It found that the average survival time of patients given drugs was the highest. What do you conclude?

• This study proves that drugs prolong life and should

  be the treatment of choice.

• We can conclude that drugs prolong life because the

  study was a comparative experiment.

• We can’t conclude that drugs prolong life because

  the patients were volunteers

• We can’t conclude that drugs prolong life because

  this was an observational study.

• We can’t conclude that drugs prolong life because no

  placebo was used.

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Tonya wanted to estimate the average amount of time that students at her school spend on Facebook each day. She gets an alphabetical roster of students in the school from the registrar’s office and numbers the students from 1 to 1137. Then Tonya uses a random number generator to pick 30 distinct labels from 1 to 1137. She surveys those 30 students about their Facebook use. Tonya’s sample is a simple random sample because

• it was selected using a chance process.

• it gave every individual the same chance to be selected.

• it gave every possible sample of the same size an

  equal chance to be selected.

• it doesn’t involve strata or clusters.

• it is guaranteed to be representative of the population.

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Consider an experiment to investigate the effective- ness of different insecticides in controlling pests and their impact on the productivity of tomato plants. What is the best reason for randomly assigning treatment levels (spraying or not spraying) to the experimental units (farms)?

• Random assignment allows researchers to generalize

  conclusions about the effectiveness of the insecticides

  to all farms.

• Random assignment will tend to average out all other

  uncontrolled factors such as soil fertility so that they

  are not confounded with the treatment effects.

• Random assignment eliminates the effects of other variables, like soil fertility.

• Random assignment eliminates chance variation in the responses.

• Random assignment helps avoid bias due to the placebo effect.

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The most important advantage of experiments over observational studies is that

• experiments are usually easier to carry out.

• experiments can give better evidence of causation.

• confounding cannot happen in experiments.

• an observational study cannot have a response variable

• observational studies cannot use random samples.

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A TV station wishes to obtain information on the TV viewing habits in its market area. The market area contains one city of population 170,000, another city of 70,000, and four towns of about 5000 inhabitants each. The station suspects that the viewing habits may be different in larger and smaller cities and in the rural areas. Which of the following sampling designs would give the type of information that the station requires?

• A cluster sample using the cities and towns as clusters

• A convenience sample from the market area

• A simple random sample from the market area

• A stratified sample from the cities and towns in the

  market area

• An online poll that invites all people from the cities and towns in the market area to participate

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Bias in a sampling method is

• any difference between the sample result and the

  truth about the population.

• the difference between the sample result and the truth about the population due to using chance to select a sample.

• any difference between the sample result and the truth about the population due to practical difficulties such as contacting the subjects selected

• any difference between the sample result and the truth about the population that tends to occur in the same direction whenever you use this sampling method

• racism or sexism on the part of those who take the sample.

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You wonder if TV ads are more effective when they are longer or repeated more often or both. So you design an experiment. You prepare 30-second and 60-second ads for a camera. Your subjects all watch the same TV program, but you assign them at random to four groups. One group sees the 30-second ad once during the program; another sees it three times; the third group sees the 60-second ad once; and the last group sees the 60-second ad three times. You ask all subjects how likely they are to buy the camera.

• This is a randomized block design, but not a

  matched pairs design.

• This is a matched pairs design.

• This is a completely randomized design with one explanatory variable (factor).

• This is a completely randomized design with two

  explanatory variables (factors).

• This is a completely randomized design with four

  explanatory variables (factors).

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A researcher wishes to compare the effects of two fertilizers on the yield of soybeans. She has 20 plots of land available for the experiment, and she decides to use a matched pairs design with 10 pairs of plots. To carry out the random assignment for this design, the researcher should

• use a table of random numbers to divide the 20

  plots into 10 pairs and then, for each pair, flip a

  coin to assign the fertilizers to the 2 plots.

• subjectively divide the 20 plots into 10 pairs (making the plots within a pair as similar as possible) and then, for each pair, flip a coin to assign the fertilizers to the 2 plots.

• use a table of random numbers to divide the 20

  plots into 10 pairs and then use the table of

  random numbers a second time to decide on the

  fertilizer to be applied to each member of the

  pair.

• flip a coin to divide the 20 plots into 10 pairs and

  then, for each pair, use a table of random numbers

  to assign the fertilizers to the 2 plots.

• use a table of random numbers to assign the two fertilizers to the 20 plots and then use the table of random numbers a second time to place the plots into 10 pairs.

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