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The document explains hypothesis testing, which compares two opposing statements about a population using sample data to determine which is more likely to be true. It defines key terms such as null and alternative hypotheses, significance level, p-value, and types of errors, and outlines the steps involved in hypothesis testing. Additionally, it provides real-life examples and a Python implementation for conducting a paired T-test to analyze the effects of a new drug on blood pressure.

Author
dev2532005
Language
EN

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