Question

Discuss the utility of Minitab with regard to variability that is inherent in studies. Identify 3 research scenarios that might provide a low, medium, and high degree of variability in the study data, and describe how the outcome of a given study can be influenced by variability in the data.

Answer 1

In many ways Minitab is a practical and effective tool used to organize, process, and

interpret data.

In discussing the utility of Minitab with regard to variability that is inherent in

studies, this information can be used for data in many ways.

Variability in reference to Minitab

refers to what can affect the outcome of the study, which can ultimately make the results more or

less significant.

This can be sample sizes, ages, genders, and more.

With this being said, the

researcher would want to be able to control the variable(s) that are involved in the study, but in

controlling the variable(s) too much control can lead to an adverse effects.

The next issue would

be that of randomization with regard to variability.

In not utilizing randomization, the researcher

has a higher probability of variability.

Variability can dramatically reduce your statistical power during hypothesis testing. Statistical power is the probability that a test will detect a difference (or effect) that actually exists.

The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design.

Consider the three situations shown in below figure. The first thing to notice about the three situations is that the difference between the means is the same in all three. But, you should also notice that the three situations don’t look the same – they tell very different stories. The top example shows a case with moderate variability of scores within each group. The second situation shows the high variability case. the third shows the case with low variability. Clearly, we would conclude that the two groups appear most different or distinct in the bottom or low-variability case. Why? Because there is relatively little overlap between the two bell-shaped curves. In the high variability case, the group difference appears least striking because the two bell-shaped distributions overlap so much.

Figure : Three scenarios for differences between means.