Indicate whether they are true or false, and briefly explain WHY. Most of the credit will be for the "why" part.

1. The key difference between experimental and observational data has to do with how the influence of confounding factors is controlled.

2. Unlike the sample frame, which researchers can choose based on their objectives and resources, the population a sample is supposed to be a sample of is simply given and can't be chosen or altered.

3. Random assignment to treatment and control groups is generally a good way to reduce the influence of confounding factors.

4. Whenever an entire quantity is divided into individual categories, a pie chart is an appropriate format for displaying how the category totals compare to one another.

5. When a study is well designed and carried out, it provides all the evidence one needs to evaluate a research hypothesis.

6. The "P" in P-value refers to the probability that the statistic in question was actually computed from the data being analyzed.

7. Researchers can often make choices that result in findings gaining or losing statistical "significance".

8. Suppose a study reports that 2/3 of the cases of childhood asthma can be attributed to air pollution, while another attributes half of these cases to indoor factors like mold and parental smoking. They are clearly in conflict; both can't be true.

9. When deciding what to include or exclude from a model, the sole consideration should be adjusted R 2 ; the higher it is, the better the model.

10. A study is performed to determine whether a new food additive is carcinogenic. The finding is that the estimated effect of ingesting this additive on the likelihood of developing cancer is not statistically significant. This is evidence that the effect is nonexistent.