How to Call B.S. on Big Data

How to Call B.S. on Big Data

7/9/2017

link

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

summary

In this article, the author provides a practical guide on how to critically analyze and call out the flaws in big data arguments. The piece highlights the growing prevalence of big data and its potential to generate misleading or false conclusions. The author presents several strategies for dealing with big data claims, including questioning the data collection methods, examining the underlying assumptions, and looking for potential biases or limitations. The article emphasizes the importance of adopting a skeptical mindset and engaging in thoughtful analysis when confronted with big data claims, rather than accepting them at face value. Overall, the guide aims to empower readers with the tools to navigate the complexities of big data and identify instances of potential misinformation.

tags

big data ꞏ data analysis ꞏ data science ꞏ statistics ꞏ data visualization ꞏ data interpretation ꞏ data manipulation ꞏ data-driven decision making ꞏ data reliability ꞏ data accuracy ꞏ data ethics ꞏ data skepticism ꞏ critical thinking ꞏ scientific method ꞏ empirical evidence ꞏ research methodology ꞏ information overload ꞏ data interpretation bias ꞏ data validation ꞏ data transparency ꞏ data privacy ꞏ data security ꞏ data literacy ꞏ data collection ꞏ data storytelling ꞏ data interpretation errors ꞏ data bias ꞏ data mining ꞏ data integrity ꞏ data quality ꞏ data aggregation ꞏ data modeling ꞏ data management ꞏ data storage ꞏ data processing ꞏ data governance ꞏ data interpretation techniques ꞏ data exploration ꞏ data inference