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Hypothesis Testing

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Hypothesis Testing PSY/315 Jenna Griffin Amber Lupo

Suppose that a doctor claims that 21 year olds have an average body temperature that is higher than the commonly accepted average human temperature of 98.6 degrees Fahrenheit. A simple random statistical sample of 25 people, each of age 21, is selected. The average temperature of the 21 year olds is found to be 98.9 degrees, with standard deviation of 0.6 degrees.
The claim being investigated is that the average body temperature of 21 year olds is greater than 98.6 degrees. This corresponds to the statement x ≥ 98.6. The negation of this is that the population average is not greater than 98.6 degrees. In other words the average temperature is less than or equal to 98.6 degrees. In symbols this is x < 98.6.
One of these statements must become the null hypothesis, and the other should be the alternative hypothesis. The null hypothesis contains equality. So for the above, the null hypothesis H0 : x = 98.6. It is common practice to only state the null hypothesis in terms of an equal’s sign, and not a greater than or equal to or less than or equal to.
The statement that does not contain equality is the alternative hypothesis, or H1 : x >98.6. The statement of our problem will determine which kind of test to use. If the alternative hypothesis contains a "not equals to" sign, then we have a two tailed test. In the other two cases, when the alternative hypothesis contains a strict inequality, we use a one tailed test. This is our situation, so we use a one tailed test.
Here we choose the value of alpha, our significance level. It is typical to let alpha be 0.05 or 0.01. For this example we will use a 5% level and alpha will be equal to 0.05. Now we need to determine which distribution to use. The sample is from a population

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