Question
In a large clinical trial, 390,196 children were randomly assigned to two groups. The treatment group consisted of 195,424 children given a vaccine for a cert
disease. The other 194,772 children were given a placebo, and 99 of those children developed the disease. Consider the vaccine treatment group to be the
a. Assume that a 0.05 significance level will be used to test the claim that
A hypothesis test is better.
b. In general, when dealing with inferences for two population proportions, which two of the following are equivalent: confidence interval method; P-value met
P-value method and critical value method are equivalent, in that they will always lead to the same conclusion. Both of these methods use a sta
the assumption that the two population proportions are equal, whereas the other method uses a standard deviation based on estimated values of the popu
c. If a 0.05 significance level is to be used to test the claim that
(Type an integer or a decimal.)
disease. The other 194,772 children were given a placebo, and 99 of those children developed the disease. Consider the vaccine treatment group to be the
a. Assume that a 0.05 significance level will be used to test the claim that
A hypothesis test is better.
b. In general, when dealing with inferences for two population proportions, which two of the following are equivalent: confidence interval method; P-value met
P-value method and critical value method are equivalent, in that they will always lead to the same conclusion. Both of these methods use a sta
the assumption that the two population proportions are equal, whereas the other method uses a standard deviation based on estimated values of the popu
c. If a 0.05 significance level is to be used to test the claim that
(Type an integer or a decimal.)
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Answer
A hypothesis test is better for testing the claim that
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Mind Expander
In statistical hypothesis testing, when you set a significance level of 0.05, you’re essentially allowing for a 5% chance of making a Type I error—rejecting the null hypothesis when it’s true. To find the corresponding confidence level, you subtract the significance level from 1. So, if you’re using a 0.05 significance level, that means a confidence level of 95% is appropriate, indicating a high degree of confidence in your results.
The choice between hypothesis testing and confidence intervals often depends on the context and the specific information needed. Hypothesis tests are more focused on decision-making (e.g., rejecting or failing to reject a hypothesis), while confidence intervals provide a range of plausible values for a population parameter. In many cases, both approaches can be complementary—using a confidence interval alongside hypothesis testing can provide a richer understanding of the data and robustness of their conclusions!
