Statistical Hypothesis Testing

Chapter 4. Hypothesis testing
The main aim of the module is to familiarize students with the theoretical knowledge of hypothesis testing and then train them in applying theory to economic practice.
After completing this module, students will be familiar with:
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the procedure of hypothesis testing; the possible outcomes in hypothesis testing; the difference between significant and nonsignificant statistical findings.

After completing this module, students will be able to:
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define what is meant by a hypothesis and hypothesis testing; understand the logic of hypothesis testing and describe the steps of hypothesis testing procedure; determine the appropriate hypothesis test to perform; formulate and write the null and alternative hypotheses; understand the meaning of the significance level and distinguish between a one-tailed and a twotailed test; test hypotheses about a population mean; state conclusions to hypothesis tests.

4.1. Introduction to hypothesis testing
A hypothesis is a statement about the value of a population parameter.
The population of interest is so large that for various reasons it would not be feasible to study all the items, or persons, in the population. An alternative to measuring or interviewing the entire population is to take a sample from the population of interest. We can, therefore, test a statement to determine whether the empirical evidence does or does not support the statement.
Hypothesis testing starts with a statement, or assumption, about a population parameter – such as the population mean. As noted, this statement is referred to as a hypothesis.
A hypothesis might be that the mean monthly commission of salespeople in retail computer stores is
$2,000. We cannot contact all these salespeople to ascertain that the mean is in fact $2,000. The cost of locating and interviewing every computer salesperson in the whole country would be exorbitant. To test the validity of the