Proportions
What type of study design is most suitable for comparing two treatments to test for a difference in outcomes?
In hypothesis testing for two proportions, what does a larger standard error indicate about our point estimate?
Why must we check that individual observations within each group are independent when conducting a hypothesis test for two proportions?
A presidential candidate wanted to know if support for her was significantly different between women and men. Her team randomly sampled 1000 voters of each gender and found that 756 of the 1000 women supported her and 560 of the 1000 men supported her. Are all three conditions of a two-proportion z-test satisfied?
A researcher conducts hypothesis tests comparing two population proportions using distinct methods resulting in different z-scores; if all else remains constant across methods except allocation ratios (sample sizes), what inference can we draw about these disparities in z-scores?
In testing the difference between two population proportions, what is the first step you should always perform before proceeding with calculations?
Suppose you conduct a significance test for the difference in two population proportions with the significance level set at 0.10. If the p-value is 0.17, what can you conclude?
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Which variability measure is used to determine the standard error for the difference between two sample proportions?
In a study comparing two treatments with binary outcomes, how does increasing the sample size impact the width of a confidence interval for the difference in population proportions?
Which method selects individual units in such a way that each unit in the population has an equal chance of being included in the sample?