hypothesis testing

# hypothesis testing - Hypothesis Testing Purpose We are...

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Hypothesis Testing Purpose: We are interested in whether or not applying a decision to a situation yields a change in effect (profits, customers, etc.) The application of a decision is called a “treatment.” We’re either interested in if applying the treatment makes no difference, or make some difference. 1. State hypotheses about the situation: Ho = Null Hypothesis (status quo or “no change” hypothesis) Should include some kind of equality (=, ≤, ≥) H1 or Ha = Alternative hypothesis (what we’re hoping is true, what we’re trying to prove) Should include inequality (≠, <, >) *** Hypotheses we’re stating are about the population, not the sample. 2. Decide on whether we’re performing a one-tailed (directional) test or a two-tailed (exploratory) test. a. If all we care about is that there was a change (positive or negative), then we have a two-tailed test. b. If we only care about a change in one direction only, then we have

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Unformatted text preview: a one-tailed test. 3. Decide on a significance level (alpha-value): .05 4. Perform our test in JMP a. Select Analyze b. Fit Y by X c. X is always the treatment column d. Y is always the values (sales, number of customers) e. Click ok f. Click on the red arrow and select “t-test” g. Depending on whether it is a one or a two tailed test, read the correct P-value 5. Decide: if P-value is less than .05, reject the Ho. . Else, you fail to reject Ho “If P is low, Null must go.” REMEMBER: We only reject or fail to reject Ho. • We could have two kinds of error. . because remember, we’re making an inference about the population. So, there is a chance we could be wrong. o If we rejected Ho, when in fact, it was true – Type I Error o If we failed to reject Ho, when in fact, it was false – Type II Error...
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• Fall '13
• Sale
• Interest, Null hypothesis, Hypothesis testing, Statistical hypothesis testing, Type I and type II errors

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