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#2 Big Data - Pitfalls of Non-Traditional Research Methods

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Manage episode 304235593 series 2992707
内容由Alena Simpson提供。所有播客内容(包括剧集、图形和播客描述)均由 Alena Simpson 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

You've probably heard how big data has changed the world by bringing hyper-customization to the forefront, discovering obscure patterns, and allowing people to react quickly to new information. In this episode Leina discusses the pros and cons of the big data revolution from a scientific standpoint. Here are the pitfalls she wants you to be aware of:

  • Big data analysis finds complex patterns quickly but cannot explain scientific phenomena. Most people are willing to accept subjective explanations to phenomena regardless of whether it is supported by evidence.
  • Research and analyses using big data is shaped around the data that is available, while the underlying causal relationship may not be captured.
  • By emphasizing correlations rather than root-causes we further impede finding effective long-term solutions.
  • "Correlation is not causation" is used selectively to confirm biases or enable cognitive dissonance. It is true, but observing patterns is part of the process of asking questions.
  • Big data is inherently dehumanizing by turning people into numbers.
  • Data fishing, also known as significance chasing, is when one approaches a research problem without a predefined hypothesis.
  • There is an increasing absence of gold standard research methodology that emphasizes the use of sample randomization, placebo groups, and blinding to minimize bias.
  • Big data quality is notoriously poor. Missing data, subjective and inconsistent definitions, and unstandardized methods of collection make the data scientist's job difficult.
  • Qualitative methods are not always taken into consideration to confirm or reject big data findings.
  • Any data can be skewed and omit information altogether.
  • Standardization is likely impossible. It should be emphasized that one should always READ THE FINE PRINT.

Leina is here to remind you that bigger is not always better. Join on Facebook, Twitter, and Instagram https://linktr.ee/leinacast

  continue reading

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Artwork
icon分享
 
Manage episode 304235593 series 2992707
内容由Alena Simpson提供。所有播客内容(包括剧集、图形和播客描述)均由 Alena Simpson 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal

You've probably heard how big data has changed the world by bringing hyper-customization to the forefront, discovering obscure patterns, and allowing people to react quickly to new information. In this episode Leina discusses the pros and cons of the big data revolution from a scientific standpoint. Here are the pitfalls she wants you to be aware of:

  • Big data analysis finds complex patterns quickly but cannot explain scientific phenomena. Most people are willing to accept subjective explanations to phenomena regardless of whether it is supported by evidence.
  • Research and analyses using big data is shaped around the data that is available, while the underlying causal relationship may not be captured.
  • By emphasizing correlations rather than root-causes we further impede finding effective long-term solutions.
  • "Correlation is not causation" is used selectively to confirm biases or enable cognitive dissonance. It is true, but observing patterns is part of the process of asking questions.
  • Big data is inherently dehumanizing by turning people into numbers.
  • Data fishing, also known as significance chasing, is when one approaches a research problem without a predefined hypothesis.
  • There is an increasing absence of gold standard research methodology that emphasizes the use of sample randomization, placebo groups, and blinding to minimize bias.
  • Big data quality is notoriously poor. Missing data, subjective and inconsistent definitions, and unstandardized methods of collection make the data scientist's job difficult.
  • Qualitative methods are not always taken into consideration to confirm or reject big data findings.
  • Any data can be skewed and omit information altogether.
  • Standardization is likely impossible. It should be emphasized that one should always READ THE FINE PRINT.

Leina is here to remind you that bigger is not always better. Join on Facebook, Twitter, and Instagram https://linktr.ee/leinacast

  continue reading

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