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Statistical hypothesis testing is a fundamental tool in data analysis, providing a structured framework to make inferences about populations based on sample data. It allows data scientists to assess whether observed patterns or differences in data are statistically significant or if they could have occurred by random chance. Hypothesis testing begins with the formulation of two competing statements: the null hypothesis (H₀), which represents a default or no-effect assumption, and the alternative hypothesis (H₁), which reflects the effect or relationship the analyst aims to detect. Through this framework, analysts evaluate evidence from data to determine whether to reject H₀ in favor of H₁ ______ ____ _________ ________ ____ __________.
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