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Statistical hypothesis testing is a fundamental tool in data analysis that allows data scientists to make inferences about populations based on sample data. At its core, hypothesis testing provides a structured framework for evaluating assumptions or claims about data by quantifying the likelihood that observed patterns are due to chance rather than an underlying effect. In the context of Data Science and Big Data, hypothesis testing helps analysts validate insights, guide decision-making, and minimize the risk of incorrect conclusions when working with massive, complex datasets. The process of hypothesis testing begins with the formulation of two competing hypotheses: the null hypothesis (H₀) and the alternative hypothesis (H₁). The null hypothesis generally represents the status quo or a statement of no effect, while the alternative hypothesis reflects the presence of a specific effect or difference. Data scientists collect sample data, compute an appropriate test statistic, and compare it to a theoretical distribution to determine whether the evidence is strong enough to reject the null hypothesis at a pre-specified significance level (α). This significance level, often set at 0.05, represents the probability of rejecting the null hypothesis when it is actually true, which is known as a Type I error. Type I and Type ___ ______ __________ __________ _____ __________ __________ ______ __________.
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