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Understanding Statistical Hypothesis Testing: A Decision-Making Framework

This chapter explores the concept of statistical hypothesis testing, a critical method for making decisions based on experimental data. A result is termed statistically significant when it is unlikely to occur by chance. The focus is on null hypothesis tests, which evaluate whether a specified population parameter equals a certain value. The analysis involves determining the probability of observing a value for the test statistic at least as large as the observed value. Applications include validating theories, models, and assessing new data against established facts.

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Understanding Statistical Hypothesis Testing: A Decision-Making Framework

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    1. 1 Chapter 8 Testing

    2. A statistical hypothesis test is a method of making decisions using experimental data. A result is called 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. Then, the test can be used to answer a question like: Assuming q0 is true, what is the probability of observing a value for the test statistic that is at least as big as the value that was actually observed? Uses of hypothesis testing: - Check the validity of theories or models. - Check if new data can cast doubt on established facts.

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