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to 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.

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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.

Therefore, the cook time for the population mean is not equal to 12 minutes; rather, it could be less than or greater than the stated value.

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.

The important point to note is that we are testing the null hypothesis because there is an element of doubt about its validity.

Whatever information that is against the stated null hypothesis is captured in the Alternative Hypothesis (H As an example related to financial markets, assume Alice sees that her investment strategy produces higher average returns than simply buying and holding a stock.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.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.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 minutes.The statistical evidence shows that either a rare event has occurred, or that the average temperature of those who are 17 years old is, in fact, greater than 98.6 degrees.A null hypothesis is a type of hypothesis used in statistics that proposes that no statistical significance exists in a set of given observations.Take another example: the annual return of a particular mutual fund is 8%.Assume that mutual fund has been in existence for 20 years.


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