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2-1 Discussion: Controls, Variables, Confounding Results To complete Part 1 of this discussion, I chose to use Experiment 1, in which Martin is researching plant growth in valleys and higher elevations. In the experiment that Martin set, the trees within the ecosystem serve as the model, as they are the species being tested and observed. The independent variable is the elevation of the transect line samples, as Martin changes this variable in his trials; the first line is set up in a wooded valley, while the second transect is located on a ridge above the valley. The dependent variable, on the other hand, is the resulting measurements of the trees along the transect lines, as Martin hypothesized that this variable will change based on the independent variable of elevation. The treatment groups are the differing elevations that are examined throughout the experiment, as this will indicate a change in the independent variable. Since Martin’s experiment does not use an elevational sample that will have known or expected results, there is no positive control group. Additionally, there appears to be no negative control group, as neither the valley nor ridge are confirmed to have no effect on tree life. When considering extraneous factors in his experiment, Martin may consider the amount of water, sunlight, and soil that is available in the different habitats, as well as the plant and animal species that may interact with the trees he is measuring. These factors could lead to a difference in tree sizes and types that are not directly caused by elevation. For Part 2 of this discussion, reading through the article and follow-up indicated to me that it is very difficult to avoid confounding variables when working with human subjects. In the study examining parental smoking, there were many measured factors that also varied among the surveyed parents and children, including the amount of exercise and television time, as well as the diets of each family (Burke et al., 1998). Some factors were not necessarily reported or surveyed, yet could also have contributed to differing results, such as how long ago the ex- smokers quit smoking, or how often each of the smokers uses cigarettes (Buncher & Morrison, 1998). Had all of these possible differences been known, there still could have been variables that were unaccounted for, such as hereditary health conditions, personal choices or preferences, and the influences of friends outside of family members. There are so many variables to consider when studying human populations that it is difficult to make accurate conclusions about the direct correlation between parental smoking and cardiovascular disease, even if there appear to be some connections and trends (Burke et al., 1998). To best control confounding variables when working with humans, plants, and animals, there are several methods that can help reduce the impact of these variables and provide the most accurate results in an experiment. First, using randomization to select subjects can reduce selection bias when assessing a population (Pourhoseingholi et al., 2012). For example, conducting a transect sample at randomly selected locations will prevent the researcher from intentionally selecting locations that have rare plants or high diversity. On the other hand, restriction can eliminate known confounders by only studying one group at a time (Pourhoseingholi et al., 2012). For example, experimenting with male groups, then female groups, may help account for differences between sexes in an experiment, as the researcher may find that the groups have differing results when tested with the independent variable. In general, it is a good idea to study and record as many confounding variables as possible before conducting the experiment, as this will provide the researcher with as much information as possible before testing the independent variable

(Pourhoseingholi et al., 2012). In using these methods, scientists can work toward controlling these extraneous variables and conducting experiments in a controlled environment whenever possible. Buncher, R., & Morrison, J. (1998). Those Confounding Variables! The Journal of Pediatrics , 133 (2), 174–175. https://doi.org/10.1016/S0022-3476(98)70214-8 Burke, V., Gracey, M., Milligan, R., Thompson, C., Taggart, A., & Beilin, L. (1998). Parental smoking and risk factors for cardiovascular disease in 10- to 12-year-old children. The Journal of Pediatrics , 133 (2), 206–213. https://doi.org/10.1016/S0022-3476(98)70221-5 Pourhoseingholi, M., Baghestani, A., & Vahedi, M. (2012). How to control confounding effects by statistical analysis. Gastroenterology and Hepatology From Bed to Bench , 5 (2), PMC4017459. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/#:~:text=To%20control %20for%20confounding%20in,stage%3B%20Stratification%20and%20Multivariate %20methods.

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