A researcher has developed a new drug designed to reduce blood pressure. In an e

Question

A researcher has developed a new drug designed to reduce blood pressure. In an experiment, 21 subjects were assigned randomly to the treatment group, and received the new experimental drug. The other 23 subjects were assigned to the control group, and received a standard, well known treatment. After a suitable period of time, the reduction in blood pressure for each subject was recorded. A summary of these data is: Treatment n=21 xbar=23.48 s=8.01 Old Drug n=23 xbar=18.52 s=7.15 The researcher suspects that the new drug results in greater average reduction in blood pressure than the old drug does. The P-value for the test described in (again, using Option 2 for degrees of freedom) is A. larger than 0.05. B. between 0.025 and 0.05. C. between 0.04 and 0.05. D. between 0.02 and 0.025.

Explanation / Answer

The t-distribution is one of the most useful statistics available to a behavioral scientist. In this tutorial, you were shown how to conduct a one sample t-test, an independent sample t-test, and a paired sample t-test. The t-test can be adapted to many other statistical questions that have not been covered. For example, a t-test is also used to assess if correlation coefficients and regression coefficients are significantly different from zero. In short, a behavioral scientist must have good working knowledge of the various uses of the t-statistic because its use is so prevalent. Part of this good working knowledge is understanding the assumptions that one makes when using a particular t-test. For example, to use the independent samples t-test presented above, it must be assumed that the variance in Population 1 is equal to the variance in Population 2. A good researcher will check the validity of important assumptions associated with the statistical test before testing the hypothesis. Having completed this tutorial, you should have a general understanding of: The t-distribution and how it differs from a normal curve The importance of the standard errors in hypothesis testing. How to estimate standard errors using sample standard deviations. Why the t-distribution is a family of curves. How critical t-values change as a function of the alternative hypothesis and the Type I error rate. How to calculate t-values for a single sample t-test, an independent samples t-test, and a paired t-test. How researchers use the observed t-values to make decisions about hypotheses.

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