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This lecture focuses on hypothesis testing within the context of Classical Linear Models. It discusses the methodology of making decisions based on experimental data, emphasizing the concept of statistical significance—indicating a result that is unlikely to have occurred by chance. The null hypothesis plays a central role in this process, allowing researchers to specify particular values for population parameters, such as q=q0. The applications of hypothesis testing include validating theories and evaluating if new data challenges established facts.
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1. 1 Lecture 4 Testing in the Classical Linear Model
2. A statistical hypothesis test is a method of making decisions using experimental data. A result is statistically significant if it is unlikely to have occurred by chance. These decisions are made using (null) hypothesis tests. A hypothesis can specify a particular value for a population parameter, say q=q0. Uses of hypothesis testing: - Check the validity of theories or models. - Check if new data can cast doubt on established facts.
