The rest of the chapter will present some specific applications of hypothesis tests as examples of the general method. The final section will extend the concept of hypothesis testing to categorical data, where we test to see if two categorical variables are independent of each other. Additionally, with the provided interactive Excel template, you will learn how the results of the examples from this chapter can be adjusted for other circumstances. The next section will give an overview of the hypothesis testing method by following along with a young decision-maker as he uses hypothesis testing. Learning the formal details of hypothesis testing will help you make better decisions and better understand the decisions made by others. Many decisions are made by thinking as though a hypothesis is being tested, even though the manager is not aware of it. As you will see, hypothesis testing, though disguised, is used in quality control, marketing, and other business applications. Hypothesis testing has many applications in business, though few managers are aware that that is what they are doing. Though the formal hypotheses are written as though you will choose with certainty between the one that is true and the one that is false, the informal translations of the hypotheses, with “almost positive” or “probably came”, is a better reflection of what you actually find. Notice that you are never entirely sure, even after you have chosen the hypothesis, which is best. Keeping this inference in mind, you can informally translate the two hypotheses into “I am almost positive that the sample came from a population like this” and “I really doubt that the sample came from a population like this, so it probably came from a population that is like something else”. Remember that you are making an inference about a population from a sample. Between the two hypotheses, all possibilities must be covered. The second, known as the alternative hypothesis, is, “The population is like something else.” It states that the population is different than the usual, that something has happened to this population, and as a result it has a different mean, or different shape than the usual case. The first, known as the null hypothesis, is basically, “The population is like this.” It states, in formal terms, that the population is no different than usual. Because the samples drawn from any population vary, you can never be positive of your finding, but by following generally accepted hypothesis testing procedures, you can limit the uncertainty of your results.Īs you will learn in this chapter, you need to choose between two statements about the population. Hypothesis testing allows you to find out, in a formal manner, if the sample supports your idea about the population. While you usually have good reasons to think it is true, and you often hope that it is true, you need to show that the sample data support your idea. In estimation, you are answering the question, “What is the population like?” While in hypothesis testing you are answering the question, “Is the population like this or not?”Ī hypothesis is essentially an idea about the population that you think might be true, but which you cannot prove to be true. Though the mathematics of hypothesis testing is very much like the mathematics used in interval estimation, the inference being made is quite different. It is different from estimation because you start a hypothesis test with some idea of what the population is like and then test to see if the sample supports your idea. Hypothesis testing is the other widely used form of inferential statistics.
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