Let’s discover the Student t-test

Please review the following article on Student t-Test. Your review should discuss the discovery of the t-test and its role in Statistics. Article: Studentttest. Your review should be between 100 and 150 words. Please post your review as a reply to this post. Your deadline to reply to this post  is April 2nd, 2015. This is an optional assignment. It is our fifth blog assignment.

How to Display Data Badly

In Wainer’s analysis entitled “How to Display Data Badly”, he emphasized 12 separate ways in which data can be presented in a misleading and inefficient way in order to encourage better practices in data display. Wainer first discusses the consequences of showing as few data points as possible. Not including data points that are essential to analysis can lead to incorrect conclusions. He then discusses the importance of using good technique when one plots data, by using appropriate grid lines and scale. His next two tips are related to visual metaphors. He points out how important shading, size, and time scales can be, as well as the order of the visual metaphors. Wainer warns against graphing data out of context. Important data can often be left out if the presenter chooses to focus on a specific interval that does not include these data points. He also cautions against changing scales in mid-axis as this can have a profound impact on the way data is interpreted by making large changes in data look less significant and vice versa. Another important point that Wainer discusses is that one should not emphasize the trivial aspects of the data while ignoring the most important findings. There are several ways to do this with the techniques he previously discussed, and should be avoided. He refers to jiggling the baseline any time one makes comparison to the control or base unclear. Another tip he gives is to try to label graphs and tables by trend or some other related factor as opposed to simply listing alphabetically or in another way that also confuses comparison. He gives a few cautions in terms of labeling, warning his readers to always make sure they are labeling legibly, completely, correctly, and unambiguously. Wainer also takes the time to discuss the potential negatives involved in the inclusion of extraneous detail into the data, such as an overwhelming number of decimal points or a huge amount of variables in one graph. Lastly, he encourages his readers to learn from example. If a graph looks particularly good and represent data exceptionally, then don’t diverge from this method! Overall, Wainer’s points are all very valid in creating an effective guide for how one can accomplish presenting data well.