In This Discussion You Will Evaluate A Research Quest 119955

In This Discussion You Will Evaluate A Research Question And Determin

In this discussion, you will evaluate a research question and determine how that question might best be analyzed. To do this, you will need to identify the appropriate application of course specified statistical tests, examine assumptions and limitations of course specified statistical tests, and communicate in writing critiques of statistical tests. A researcher wishes to study the effect of a new drug on blood pressure. Consider and discuss the following questions as you respond: Would you recommend using a z -test, a t -test, or an ANOVA for the analysis? Explain your answer. What would your choice of test depend on? For the test you select, explain your design and your comparison groups. Would the hypothesis be directional or non-directional? Would the test be one-tailed or two-tailed? What would be the null and what would be the alternative hypothesis?

Paper For Above instruction

The appropriate choice of statistical test for analyzing the effect of a new drug on blood pressure primarily depends on the specific research design, the number of groups involved, and the nature of the data collected. In this scenario, determining whether to employ a z-test, t-test, or ANOVA requires understanding these factors thoroughly to ensure accurate and valid statistical conclusions.

If the study involves comparing the blood pressure of two independent groups—such as a treatment group receiving the new drug and a control group receiving a placebo—a t-test would generally be appropriate. Specifically, an independent samples t-test would be suitable if the data meet assumptions of normality and homogeneity of variance. The t-test is advantageous here because it is designed to compare the means of two groups and is effective for small to moderate sample sizes, assuming the data are reasonably normally distributed.

Conversely, if the research involves more than two groups—for instance, testing multiple doses of the drug or comparing different medications—a one-way ANOVA (Analysis of Variance) becomes appropriate. ANOVA allows for the comparison of procedures across multiple groups simultaneously, reducing the risk of Type I error associated with conducting multiple t-tests, which increases the likelihood of false positives. The assumptions for ANOVA include normality, homogeneity of variances, and independent observations.

The z-test is generally reserved for situations involving large sample sizes (typically n > 30), where the population variance is known or when the sample mean distribution approximates normality under the Central Limit Theorem. Considering the typical design of drug studies where population variance is rarely known and sample sizes may be small or moderate, the z-test is less commonly applicable unless such conditions are met.

The choice of the statistical test depends heavily on the experimental design—such as whether the data involve independent or paired samples, the number of groups, and whether variances are known or assumed equal. For example, if the data are from a repeated measures design where blood pressure is measured before and after administering the drug within the same subjects, a paired t-test would be appropriate. On the other hand, for independent groups, the independent t-test is suitable.

Regarding hypotheses, the test hypotheses can be either directional or non-directional. A non-directional hypothesis suggests that the drug may have an effect, but does not specify the direction of that effect (i.e., whether blood pressure increases or decreases). Conversely, a directional hypothesis predicts the specific effect, such as "the drug lowers blood pressure." Depending on the research question, the hypothesis can also be tested with a one-tailed or two-tailed test. Usually, if the hypothesis predicts a specific direction (e.g., blood pressure will decrease), a one-tailed test would be appropriate; otherwise, a two-tailed test should be used to examine any difference regardless of direction.

In sum, the selection of the statistical test hinges on the number of groups involved, the nature of the data, and the design of the study. The t-test is suitable for comparing two groups with continuous data, ANOVA for multiple groups, and the z-test in specific situations with large samples and known variances. Clarifying the hypotheses and deciding whether to use one-tailed or two-tailed tests further guides analysis and interpretation, ensuring the results align with the research questions and assumptions.

References

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