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A Complete Guide to A/B Testing in Python: p-Values, Z-Tests, and Business Metrics
Oftentimes, when we compare two versions of a product, it’s difficult to tell whether the changes made in the alternative version are actually producing the expected results or if the differences we see could simply be due to chance. This is where statistical significance comes in; it represents the idea of measuring how likely it…
A/B Testing Explained: Metrics, Methods, and Why It Matters
A/B tests are designed to answer targeted business questions with evidence instead of guesswork. Once a problem is detected (such as a drop in sales) the team proposes hypotheses to explain what might be driving that change. From there, a second version of the product or feature is created, ideally with small and controlled adjustments.…
A Practical Guide to Quantitative UX Research
When we talk about UX research, there are several methods we can use to understand how people interact with a product. These methods are usually grouped into behavioral (how users act when interacting with the product) and attitudinal (what users think, say, or feel about that interaction). They can also be categorized as qualitative or…
A/B Testing AI AI Basics Artificial Intelligence Classification Classification Algorithm Cross-validation Data Analytics Data Science Deep Learning Exploration Rate Fundamentals K-Means Machine Learning ML for Beginners Model-Free Learning Overfitting Python Q-Learning Regularization Reinforcement Learning Relational Database SQL Support Vector Machine SVM UX Research
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