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The fourth and final step is to analyze the results and either accept or reject the null hypothesis.Here is a simple example: A school principal reports that students in her school score an average of 7 out of 10 in exams.For example, if the hypothesis test is set up so that the alternative hypothesis states that the population parameter is not equal to the claimed value.

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The next step is to formulate an analysis plan, which outlines how the data will be evaluated.

The third step is to carry out the plan and physically analyze the sample data.

Take another example: the annual return of a particular mutual fund is 8%.

Assume that mutual fund has been in existence for 20 years.

We take a random sample of annual returns of the mutual fund for, say, five years (sample) and calculate its mean.

We then compare the (calculated) sample mean to the (claimed) population mean to verify the hypothesis.This is our situation, so we use a one-tailed test.We conclude by stating the results of our hypothesis test.Statistical hypotheses are tested using a four-step process.The first step is for the analyst to state the two hypotheses so that only one can be right.The null hypothesis is the initial statistical claim that the population mean is equivalent to the claimed.For example, assume the average time to cook a specific brand of pasta is 12 be true – the way a defendant in a jury trial is presumed innocent until proven guilty by evidence presented in court.Similarly, hypothesis testing starts by stating and assuming a “null hypothesis,” and then the process determines whether the assumption is likely to be true or false.The following shows a worked out example of a hypothesis test.Suppose that a doctor claims that those who are 17 years old have an average body temperature that is higher than the commonly accepted average human temperature of 98.6 degrees Fahrenheit.


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