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•Calculate Test Statistic, z* •The test statistic measures how different the sample proportion we have is from the null hypothesis •We calculate the z-score by assuming that is the population proportion 𝑧∗= ( − ) 1− 8
Identify the population, comparison distribution, inferential test, and assumptions. State the null and research hypotheses. Whether this is the whole population or a control group, we need to find the mean and some measure of spread (variability). Determine critical values or cutoffs. How extreme must our data be to reject the null?
When we conduct a test about a population proportion, we are performing a Large Sample Z test. The test statistic for this Large sample z test has the following form:
How do we interpret the z-score for a sample mean in the context of our hypothesis test? • Our sample mean is __________ standard errors below / above the null hypothesized mean. We should find a p-value of about 0.029 (or 0.29%). Which statement correctly interprets 0.29%? 1.
Two sample hypothesis testing and confidence interval In previous chapters, we only consider the inference about a single mean, or a single proportion. Now we extend the study to comparison between the means/proportions/variance of two population. For example, a hypothesis testing question: H0:μ1=μ2 vs H0:μ1≠μ2. Two sample z-test for mean ...
This set of notes shows how to use Stata to conduct a hypothesis test about the population mean of a quantitative variable or the population proportion for a dichotomous variable.
Since the proportion tests of this chapter use the z-statistic, you use table A (standard normal probabilities) to calculate the p-value . Later, you will use the calculator to find the p-value .
