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Big data ethics, also known simply as data ethics, refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data, in particular personal data. [1] Since the dawn of the Internet the sheer quantity and quality of data has dramatically increased and is continuing to do so exponentially.
Data Feminism is a book written by Catherine D’Ignazio and Lauren F. Klein as part literature review, part call to action, Data Feminism provides a framework for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science.
Publication data: 1820 (3rd ed.) Online version: Internet Archive ; CNRS , with more accurate character recognition; Gallica-Math , complete PDF and PDFs by section Description: Introduced the Laplace transform , exponential families , and conjugate priors in Bayesian statistics .
In chapter one of her book, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy," Cathy O’Neil talks about mathematical models as being mere simplifications ...
Unobtrusive research (or unobtrusive measures) is a method of data collection used primarily in the social sciences.The term unobtrusive measures was first coined by Webb, Campbell, Schwartz, & Sechrest in a 1966 book titled Unobtrusive Measures: Nonreactive Research in the Social Sciences. [1]
With the application of probability sampling in the 1930s, surveys became a standard tool for empirical research in social sciences, marketing, and official statistics. [1] The methods involved in survey data collection are any of a number of ways in which data can be collected for a statistical survey. These are methods that are used to ...
In contrast with other forms of ethical misconducts, the debate over research integrity is focused on "victimless offence" that only hurts "the robustness of scientific record and public trust in science". [3] Infractions to research integrity include chiefly "data fabrication, falsification, or plagiarism". [3]
Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.