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The Bonferroni correction is a statistical adjustment used when you perform multiple hypothesis tests at once. When you run many hypothesis tests, the chance of making at least one Type I error (a false positive) increases. The Bonferroni correction helps control the overall error rate so that it stays below your chosen significance level (α). To make a Bonferroni correction, we set the significance level for each individual test equal to: , where is the number of tests being performed, is overall desired Type 1 error rate, and is the significance level to be used for each individual test. For example, consider the case of a marketing analyst that is testing four different advertising strategies (A, B, C, and D) to determine whether any of them increase average weekly sales. If we desire an overall Type 1 error rate no larger than , then the significance level to be used for each individual test is . Assuming none of the 4 different advertising campaigns worked, what is the overall probability of a committing a Type 1 error (at least one Type 1 error), if we set ? Round to 3 decimals.