This preview shows pages 1–6. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Click to edit Master subtitle style 1/14/11 Hypothesis Testing 1/14/11 2Slide 1 2 Hypotheses Hypotheses are working models that we adopt temporarily. Our starting hypothesis is called the null hypothesis . The null hypothesis, that we denote by H0, specifies a population model parameter of interest and proposes a value for that parameter. We usually write down the null hypothesis in the form H0: parameter = hypothesized value. The alternative hypothesis, which we denote by HA, contains the values of the parameter that we 1/14/11 3Slide 1 3 Testing Hypotheses The null hypothesis , specifies a population model parameter of interest and proposes a value for that parameter. We might have, for example, H0: p = .35. We want to compare our data to what we would expect given that H0 is true. We can do this by finding out how many standard deviations away from the proposed value we are. We then ask how likely it is to get results like we did if the null hypothesis were true. 1/14/11 4Slide 1 4 The statistical twist is that we can quantify our level of doubt. We can use the model proposed by our hypothesis to calculate the probability that the event weve witnessed could happen. Thats just the probability were looking forit quantifies exactly how surprised we are to see our results. PValues 1/14/11 5Slide 1 5 When the data are consistent with the model from the null hypothesis, the Pvalue is high and we are unable to reject the null hypothesis. In that case, we have to retain the null hypothesis we started with....
View Full
Document
 Spring '10
 COLLINS

Click to edit the document details