13 Reason's Why Articel

Read this article: https://www.theatlantic.com/entertainment/archive/2017/08/13-reasons-why-demonstrates-cultures-power/535518/

Secondary Knowledge Claims:

Increased attention towards suicide and how the larger awareness is brought to it the greater the rates of suicide would be.

“increased coverage of suicide in the media leads to a related increase in suicide attempts.”

Case studies and sample studies reflect the group as a whole.

“An editorial published at JAMA commenting on the study stated that teens are particularly vulnerable when it comes to binge viewing.”

Correlation is causation. The use of statistical data is one of justifying the harsh analyses and appears to have taken in two events around the time and discounted them instead of eliminating other factors, such as season, global events, and region of search.

“JAMA Internal Medicine, used Google Trends to monitor certain search terms regarding the subject of suicide, like “how to commit suicide,” “suicide hotline number,” and “teen suicide.” Seventeen out of the top 20 searches were significantly elevated, and the biggest increases came with terms related to suicidal thoughts and ideation, like “how to kill yourself.” The time period for searches ended on April 18 to preclude the suicide of the former NFL player Aaron Hernandez, which could have influenced data, and any searches related to the movie Suicide Squad were discounted.”

RQ:

How do we interpret data in order to form knowledge?

To what extent should we rely on correlation to formulate arguments and justify ramifications?

When often reading articles we run across many firm stances often backed up by the studies, and other primary sources. However, when examining these studies and primary sources you might find that many of them will not be directly linked to the articles purpose and or rely on correlations as the main backbone of the argument or answer.

For example when analyzing large groups in order to create an argument which the data “directly” answers and follows the question and gives the almost desired answer. When analyzing search results of scary clowns and other related monsters, in order to analyzing the impact of the newest adaptation Stephen King’s IT. We see the study follows search queries for clowns, monster, and Stephen king, while excluding searches for It or other results that are two words which one is IT. These parameters are emplaced in order to prevent skewing of data, according the analyzers. While seemingly responsible and justified this research excludes many other parameters which should be emplaced in order to give better and more accurate results that follow a more direct correlation which should be more on the line of causation. Such as questionnaires querying people who research topics similar to the movie IT this will help show and justify the argument that these results are due to the movie and no other factors.

This commitment and time which it would take in order to get these results is far greater than the reward, as well as unrealistic, most people would avoid answering questionnaires and would often not spend the time to give much thought into them. Research studies are very difficult to find direct correlations, and even when there is a very seemingly apart examples they are often nearly impossible to trace back to a main source and have too many factors that impact the result, which make it nearly impossible to prove causation. An example of high correlation which will never achieve causation is Smoking and increased Cancer rates, they display great positive correlation. Many correlations are very reliable and do give great insight and better understanding of a situation and links between seemingly unrelated subjects, we cannot leave the all our faith on these correlations and must be demandingly selective when analyzing and studying research.