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In the realm of statistics and data science, hypothesis testing is a fundamental procedure used to make inferences or draw conclusions about a population based on sample data. Central to hypothesis testing are two types of errors: Type I and Type II errors. Understanding these errors is crucial for interpreting results and making informed decisions.
Understanding and managing Type I and Type II errors is essential for conducting robust hypothesis tests and making reliable decisions based on statistical data.