Too often, statistics are used to 'prove' some point or to persuade an audience to some particular point of view, without really being accurate, complete, or honest. This issue has been the subject of numerous texts. You may be interested in reading such titles as: Damned Lies and Statistics, or How to Lie with Statistics. To fulfill this discussion board activity, complete the following:
Research one example where data analysis might have been misused or misapplied. Read about the example thoroughly so that you understand how analytics was used, and why it was problematic.
Share your chosen example, documenting your source. Be sure to summarize the scenario thoroughly.
Explain why use of analytics contributed to the problem.
Discuss the consequences of the matter. Did the company/organization involved suffer any adverse consequences? If so, were the related to public opinion/trust, were they financial, were they punitive, etc.?
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Example: The Case of A/B Testing Misuse by Facebook
Source: The Guardian - "Facebook sorry for 'millennial' targeting error" (https://www.theguardian.com/technology/2017/nov/11/facebook-sorry-for-millennial-targeting-error)
Summary:
In 2017, Facebook faced criticism when it was reported that the social media giant had allowed advertisers to target teenagers on the platform who were feeling "insecure" or "worthless." This issue arose as a result of Facebook using data analytics to categorize users' emotional states for the purpose of ad targeting. The company had offered advertisers the ability to target young users who exhibited emotions such as feeling "stressed," "anxious," or "defeated." The feature relied on the analysis of posts, comments, and likes to determine users' emotional state.
Analytical Contribution to the Problem:
The use of analytics in this case contributed to the problem by allowing advertisers to target vulnerable individuals based on their emotional states. While Facebook aimed to leverage user data analysis to provide advertisers with more targeted marketing opportunities, the implementation of such analytics without proper ethical considerations led to potential exploitation and potentially harmful consequences.
Consequences:
Following the revelation of allowing advertisers to target vulnerable teenagers based on emotional states, Facebook faced significant backlash. The incident resulted in a loss of public trust and raised questions about user privacy and the platform's ethical standards. Advertisers using the targeting feature faced criticism as well, contributing to negative public opinion regarding their practices. Although Facebook did not suffer substantial financial repercussions directly related to this incident, they faced increasing scrutiny from users, regulators, and the media, ultimately leading to stricter regulations and several subsequent controversies regarding data privacy.
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