Stop guessing whether engagement survey differences matter—learn to test them statistically with AI assistance. In this hands-on course, you'll discover why not every score gap deserves a reaction, mastering the concept of statistical significance that separates evidence-based HR decisions from gut reactions. You'll learn to run Excel's T.TEST function to compare department scores, use ChatGPT to interpret your p-value in plain language, and apply the standard 0.05 threshold to classify differences as significant or not significant. Critically, you'll learn to verify AI outputs against your actual data—ensuring accuracy while gaining efficiency. Through realistic role plays and coach dialogues, you'll practice explaining concepts to colleagues and completing AI-assisted analyses. Designed for HR generalists, HR analysts, and people managers who want to combine analytics with modern AI tools. Basic Excel familiarity helpful; no statistics background required.
Learn why not every engagement score difference deserves a reaction—and how statistical significance helps you distinguish real patterns from random noise. Understand what p-values mean, learn the 0.05 decision threshold, and prepare to run your first t-test by understanding Excel's T.TEST function…
Move from understanding to doing: run an actual t-test in Excel, use AI to interpret and explain your p-value, apply the 0.05 threshold, classify the result as significant or not significant, and document your findings with AI-assisted plain-language explanations.
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Coursera is in Tier 4: commercial training companies and platform-made courses of our institution ranking (56/100). Made by the platform, often short guided projects.
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
Harvard University
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
Harvard University
An introduction to the intellectual enterprises of computer science and the art of programming.
