Data Science (Math Problem Sample)

Data Science and Statistics Course No: Student’s Name: Institution: Date: Understanding of Statistics Introduction Proper statistical interpretations are vital in the sense that the information is depended upon by many bodies that make major decisions from the results obtained from these studies. Therefore, whether the misinterpretation is deliberate or not, the harm caused carries the same weight. Deliberate statistical misinterpretations are mainly done by people who plan on benefiting from the cooked results or taking advantage of their target. Non-deliberate misinterpretation could result from a misunderstanding of the data or using incomparable definitions, through which the harm caused could still carry as much weight as deliberate misinterpretation. Misunderstanding the data is a major cause of statistical misinterpretations, especially in a correlation and causation relationship. While the results between two variables being tested might indicate a correlation between the two, one has to be vigilant enough to differentiate the possibility of one variable causing or affecting the outcome of the other (Hartman, Hunt & Childers, 2013). A perfect example to illustrate this is the 2018 Lancet Public Health study on the relationship between mortality rate and carbohydrate intake. From the results obtained, it is evident that a shorter life span was observed in persons who had low and high carbohydrate consumption levels, compared to those who had moderate carbohydrate consumption (Seidelmann et al., 2018). The study concludes that moderate carbohydrate intake subsequently lengthens the lifespan of an individual. However, this is not the case. These results were merely observational, and there is no proof of causation between the two variables. The lifespan of an individual is subject to many other factors which cannot be assumed to be constant in the analyses. The effective conclusion to draw from the study is that there exists a correlation between the two variables, but not causation. Incomparable definitions is a major cause of statistical misinterpretations, especially in instances where the data was collected over a wide range of possibilities. For instance, when computing marriage statistics, marriage comprises those who are legally married and living together, those who are married under the common law, and those legally married but are currently separated. The 2021 statistics comparing the number of married men and women in the United States show that 67.54 million men were married, while 68.33 million women were married (Duffin, 2022). Logically, the number of married women and men would be expected to be equal. This inconsistency could result from the failure to separate these three instances of marriage. While some would consider themselves married through common law, some would not. While some would consider themselves unmarried after physical separation from their legally married spouse, some would not. It is therefore vital to put into consideration incomparable definitions when carrying out statistical ana...