A/B Testing


A/B testing is a statistical hypothesis testing method used to compare two versions of a product or webpage to determine which one performs better. In the context of data science and artificial intelligence, A/B testing is commonly used to evaluate the effectiveness of machine learning models or algorithms. A/B testing involves randomly dividing a sample population into two groups, where one group is shown the original version (control group) and the other group is shown the modified version (treatment group). The performance of each group is then measured and compared using statistical analysis to determine if the modified version is statistically significant in terms of performance improvement. A/B testing is an important tool in data-driven decision making, as it allows for objective evaluation of changes and improvements to a product or system.


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