Researchers are investigating whether people who exercise with a training partner have a greater increase, on average, in targeted exercise intensity compared with people who exercise alone. Two methods of collecting data have been proposed.

ﾷ Method I: Recruit volunteers who are willing to participate. Randomly assign each participant to exercise with a training partner or to exercise alone.

ﾷ Method II: Select a random sample of people from all the people who exercise at a community fitness center. Ask each person in the sample whether they use a training partner, and use the response to create the two groups.

(a) For each method, the researchers will record the change in targeted exercise intensity for each person in the investigation. They will compare the mean change in intensity between those who exercise with a training partner and those who do not.

(i) Describe the population of generalization if method I is used.

(ii) Describe the population of generalization if method II is used.

(b) Suppose the investigation produces a result that is statistically significant using both methods. What can be concluded if method I is used that cannot be concluded if method II is used?

(i) The population of generalization if the method I is used will be all people who are willing to determine the effect of exercising with a training partner as compared to exercising alone.

(ii) The population of generalization, if method II is used, will be all people who exercise at a community fitness center.

(b)

If both the methods produce significant results then the difference between the conclusion of the method I and the conclusion of method II is that the result of the method I can be generalized to a broader population since it does not restrict people to participate in the study.

facundo3141592 facundo3141592 · Answer 2 · 2021-11-17T13:51:22+01:00

Here we must compare two sampling methods for a given experiment, the answers are:

a) i) The population generalization, in this case, is almost random, the only problem here is that only people that want to participate will participate, so is likely that the sample is not totally randomized, and that there is a larger number of people that regularly does exercise than people that do not do exercise.

ii) This case is less random than the previous, here all the people in the sample regularly do exercise, and these people can choose if they prefer to work with a partner or not. So the sample is biased.

b) In method 1 we can conclude that the results apply to the whole population, while in method 2 we can't conclude that, just because the sample is biased.

So we have two sampling methods:

Method I: Recruit volunteers who are willing to participate. Randomly assign each participant to exercise with a training partner or to exercise alone.

So here we only have people that want to participate, but that people do not necessarily do exercise in their normal day-to-day life. People also can't choose to work out in a team or alone.

Method II: Select a random sample of people from all the people who exercise at a community fitness center. Ask each person in the sample whether they use a training partner, and use the response to create the two groups.

Here we already know that all the members in the sample are people that do exercise regularly, and we ask the people if they work alone or with a partner, an in base of that, we create the two groups (as I understand, here people can choose if they want to work alone or with a partner). So this is a more biased sample.

a) i) The population generalization, in this case, is almost random, the only problem here is that only people that want to participate will participate, so is likely that the sample is not totally randomized, and that there is a larger number of people that regularly does exercise than people that do not do exercise.

ii) This case is less random than the previous, here all the people in the sample regularly do exercise, and these people can choose if they prefer to work with a partner or not. So the sample is biased.

b) In method 1 we can conclude that the results apply to the whole population, while in method 2 we can't conclude that, just because the sample is biased.

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