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  1. Why you sometimes need to break the rules in data viz
  2. The Official Coronavirus Numbers Are Wrong, and Everyone Knows It
  3. Airbnb has devoured London – and here’s the data that proves it
  4. How Machine Learning Pushes Us to Define Fairness
  5. Data Have a Limited Shelf Life
  6. The ASA Statement on p-Values: Context, Process, and Purpose
  7. Moving to a World Beyond “p < 0.05”
  8. What Have We (Not) Learnt from Millions of Scientific Papers with p Values?
  9. Abandon Statistical Significance
  10. What Statistics Can and Can’t Tell Us About Ourselves
  11. Information and Uncertainty:  Two Sides of the Same Coin
  12. Data for the Public Good
  13. p-Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting
  14. Understanding Database Reconstruction Attacks on Public Data
  15. Paying attention to how algorithmic systems impact marginalized people worldwide is key to a just and equitable future
  16. Algorithms Designed to Fight Poverty Can Actually Make It Worse
  17. Big data problems we face today can be traced to the social ordering practices of the 19th century
  18. Storks Deliver Babies (p< 0.008) pdf
  19. Spurious Correlation
  20. A statement about data
  21. The Parable of Google Flu: Traps in Big Data Analysis
  22. The Will Rogers Phenomenon — Stage Migration and New Diagnostic Techniques as a Source of Misleading Statistics for Survival in Cancer
  23. Why most published scientific results are false
  24. Why Big Data Isn’t Enough
  25. How statistics lost their power – and why we should fear what comes next
  26. The pulse of the people
  27. Beyond prediction: Using big data for policy problems
  28. Road Map for Choosing Between Statistical Modeling and Machine Learning
  29. Economic predictions with big data: The illusion of sparsity
  30. Graphical interpretations of data: Walking the line
  31. Analyzing ordinal data with metric models: What could possibly go wrong?
  32. Putting data back into context
  33. But Shouldn’t That Work Against Me?
  34. I’m a data scientist who is skeptical about data
  35. What is the Purpose of Statistical Modelling?
  36. Why is Data Visualization Important? What is Important in Data Visualization?