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#ABTesting

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LavX News

The Hidden Dangers of A/B Testing: How Data-Driven Decisions Can Lead to Enshittification

As software developers increasingly rely on A/B testing to guide product decisions, a troubling trend emerges: the risk of enshittification. This article explores how a focus on metrics over user expe...

news.lavx.hu/article/the-hidde

The Hidden Dangers of A/B Testing: How Data-Driven Decisions Can Lead to Enshittification
#news#tech#UserExperience
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ResearchBuzz: Firehose

University of British Columbia: Facebook is constantly experimenting on consumers — and even its creators don’t fully know how it works. “Users of social media platforms like Facebook, Instagram and TikTok might think they’re simply interacting with friends, family and followers, and seeing ads as they go. But according to research from the UBC Sauder School of Business, they’re part […]

https://rbfirehose.com/2025/03/11/university-of-british-columbia-facebook-is-constantly-experimenting-on-consumers-and-even-its-creators-dont-fully-know-how-it-works/

#abtesting#advertising#consumerprotection
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Kerrick Long (code)

High Performance PostgreSQL for Rails by Andrew Atkinson is part of a 16-ebook $18 bundle! Pragmatic Bookshelf and Humble Bundle have made it available along with Practical A/B Testing, Designing Data Governance from the Ground Up, SQL Antipatterns Vol. 1, and others. Pretty sweet deal!

humblebundle.com/books/data-en

Humble BundleHumble Tech Book Bundle: Data Engineering and Management by PragmaticLearn all about data engineering and management with our latest collection of tech courses. Pay what you want & support Save the Children.
#Ruby#Rails#PostgreSQL
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Dr Mircea Zloteanu 🌼🐝

#statstab #260 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data

Thoughts: "A_w and d_r were generally robust to these violations"

#robust #effectsize #ttest #2groups #metaanalysis #assumptions #ttest #cohend

link.springer.com/article/10.3

SpringerLinkEffect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data - Behavior Research MethodsIn psychological science, the “new statistics” refer to the new statistical practices that focus on effect size (ES) evaluation instead of conventional null-hypothesis significance testing (Cumming, Psychological Science, 25, 7–29, 2014). In a two-independent-samples scenario, Cohen’s (1988) standardized mean difference (d) is the most popular ES, but its accuracy relies on two assumptions: normality and homogeneity of variances. Five other ESs—the unscaled robust d (d r * ; Hogarty & Kromrey, 2001), scaled robust d (d r ; Algina, Keselman, & Penfield, Psychological Methods, 10, 317–328, 2005), point-biserial correlation (r pb ; McGrath & Meyer, Psychological Methods, 11, 386–401, 2006), common-language ES (CL; Cliff, Psychological Bulletin, 114, 494–509, 1993), and nonparametric estimator for CL (A w ; Ruscio, Psychological Methods, 13, 19–30, 2008)—may be robust to violations of these assumptions, but no study has systematically evaluated their performance. Thus, in this simulation study the performance of these six ESs was examined across five factors: data distribution, sample, base rate, variance ratio, and sample size. The results showed that A w and d r were generally robust to these violations, and A w slightly outperformed d r . Implications for the use of A w and d r in real-world research are discussed.
#psychology#statistics#research
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