# Section_6_2 - Sec 6.2 Tests of Signicance Typical situation...

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Sec. 6.2 - Tests of Significance Typical situation in a test of significance: There is some assumed value of the parameter. This is known as the null hypothesis, written H o . You have reason to believe that the assumed value of the parameter is incorrect. This is known as the alternative hypothesis, written H a . Sometimes your reason to believe that H 0 is wrong simply suggests that the parameter value is not what it is assumed to be. This is known as a two-sided alternative hypothesis . Other times your reason to believe that H 0 is wrong suggests that the true value of the parameter is either greater or smaller than the assumed value. This is known as a one-sided alternative hypothesis . Example 1 Last year, your company’s service technicians took an average of 1.8 hours to respond to trouble calls from business customers who had purchased service contracts. Does this year’s data show a different average response time? Example 2 Suppose that the national average for Math SAT scores is 511 with a standard deviation of 105, but a sample of 225 students in New York has an average of 526 on the Math portion of their SAT. Is this enough evidence to conclude that the average Math score on the SAT among all New York students is higher than the national average? Which would be more convincing a result based on 225 students, or on 2 students? Which would be more convincing a sample score of 512, or of 526?