This is your last opportunity for a blog entry. Your deadline for posting a reply is April 30th. Please prepare a powerpoint presentation of your project and post it here. This is an optional blog post. If you use a dataset, you would like to publish your results in the future, please do not post your presentation here (just send me a private e-mail with the Subject line: Blog post)

Common mistakes in Statistics

What can go wrong in a statistical study? Many things, there’s even  a book on this subject

Your next assignment is to post an article or a video( could be anything: peer reviewed journal articles, newspaper articles, book examples, youtube videos, TED Talks…) that discusses not so correct ways of doing statistics or interpreting statistical results. Please include a short review of the “bad statistics” discussed in the post .

Please post your assignments as a reply to this message. Due date: April 16th 2015

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.

Biostatistics and Clinical Decision Making

While my video discusses the role of biostatistics at Johns Hopkins, it still was able to shed some light on the importance of the field. What it focused on was how it is used to understand what is true about a system or more specifically a type of medical condition. This can be done based on the large amounts of data that has become available through experiences with populations of patients. For example a biostatistician can sift through a messy cloud of statistics in regards to characteristics that a population of patients have and use this information to help current and future patients. From their analysis of patient population statistics, they can also deduce whether or not the findings for a large-scale population can be applied directly to an individual patient. If they find that there is not a direct correlation between population and patient, they can then deduce a relationship and adjust their findings so that they know how to apply population-based knowledge to an individual. Lastly they utilize the integration of various practices of clinical science and develop a dialogue amongst them to create ideas about what may or may not be true in regards to a system or disease and from there they are able to carry out testing to prove or disprove their hypotheses. In summary they utilize biostatistics to understand medical conditions on a large scale to better decide what to do for each patient.


Public Health & Biostatistics

Please provide an example of a video presentation (YouTube, TED Talks) on the use of Biostatistics in solving and/or analyzing public health problems. In addition, you should write a short summary of the talk (between 100 and 150 words) in your own words. Please provide the link of the original talk and your summary as a reply to this message. You cannot review a video talk that was already posted by someone else. Due Date: 9pm, Feb 12th