How to Call B.S. on Big Data

How to Call B.S. on Big Data

7/5/2017

link

https://www.newyorker.com/tech/elements/how-to-call-bullshit-on-big-data-a-practical-guide/amp

summary

This article from The New Yorker provides a practical guide on how to critically evaluate and "call bullshit" on the use of big data. The author argues that while big data has gained immense popularity, it is often misunderstood and misused. The article highlights the importance of questioning the validity and reliability of data sources, as well as being aware of biases and limitations in data collection methods. It also emphasizes the need to consider context, ask the right questions, and seek out experts in order to avoid falling for misleading or exaggerated claims based on big data. Overall, the article aims to equip readers with the tools necessary to approach big data with a skeptical mindset and make informed judgments.

tags

big data ꞏ data analysis ꞏ data science ꞏ statistical analysis ꞏ data interpretation ꞏ data visualization ꞏ data-driven decision making ꞏ data collection ꞏ data accuracy ꞏ data manipulation ꞏ data sources ꞏ data mining ꞏ data bias ꞏ data validity ꞏ data integrity ꞏ data quality ꞏ data ethics ꞏ data skepticism ꞏ critical thinking ꞏ research methodology ꞏ scientific method ꞏ information overload ꞏ information literacy ꞏ information credibility ꞏ information sources ꞏ information validation ꞏ information manipulation ꞏ information bias ꞏ information reliability ꞏ information integrity ꞏ information quality ꞏ information ethics ꞏ information skepticism ꞏ technology ꞏ digital era ꞏ information age ꞏ information revolution ꞏ information society ꞏ information management ꞏ information security ꞏ information privacy ꞏ digital literacy ꞏ media literacy ꞏ data reliability ꞏ data privacy ꞏ data security ꞏ data governance ꞏ data transparency ꞏ data trust ꞏ skepticism ꞏ critical analysis ꞏ fact-checking ꞏ misinformation ꞏ disinformation ꞏ pseudoscience ꞏ statistical fallacies