From margins of error to decision risks, here are four essential ways statisticians measure uncertainty.
Matthew Mayo (@mattmayo13) holds a master’s degree in computer science and a graduate diploma in data mining. As managing editor of KDnuggets & Statology, and contributing editor at Machine Learning Mastery, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, language models, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
It is arithmetic: every additional test raises the probability that at least one result clears the significance threshold on chance alone, even when no real effect exists anywhere in the data.
Introduction: The Strange Feeling of “This Is Wrong… But I’ll Keep Reading” You have undoubtedly experienced this scenario: You open a newspaper, website, or a…
This article provides a practical explanation of the Dunning-Kruger Effect, how it specifically manifests in analytics workflows, and how to build a culture that values calibration over false certainty.
Use this interactive visualizer app to explore the concept of the Central Limit Theorem firsthand.
This article highlights ten of the most valuable (and free) newsletters that are indispensable for working data-driven professionals.
With base rate neglect, we get so caught up in the details of an individual example case that we forget how common or rare that case is in the bigger picture.
Recognizing confirmation bias is an important elementary step toward building a more robust and honest analytical practice.
Here are five crucial assumption checks you should always perform, no matter the data work you are carrying out.
This article will demonstrate a better way: loop optimization with Cython.