Attachment **In-Patient-Costs-for-Neurological-Disorders.pptx**

Here is my Power Point presentation! Note: It does not go into as much detail as my report, so as not to overwhelm the slides.

]]>This document lists numerous ways in which statistics can be used improperly, from making up numbers, to misleading readers, to ignoring the baseline, or selection bias. In each of the many types of errors, examples are given, most taken from newspaper clippings to provide a real illustration, such as the pentagon claiming to have shot down 41 Iraqi missiles when actually the number was only 4. Another example showed a map with different sections shaded according to total crimes committed in those areas, but it did not account for population of those areas.

]]>This article was published in Nature and discusses the errors in misinterpreting the p-value. The author introduces the topic by talking about a study conducted in 2010 looking at the difference between extremists’ and moderates’ interpretation in ambiguous topics i.e. not black and white. Motyl’s group got a p-value0.05. However, the issue had not been the data or the analysis but rather the heavy reliance in significance in the p-value. At this point, the article shifts to the history of the p-value with Ronald Fisher. Fisher intended for the p-value to be used as an indication to reevaluate data. This statistic was not meant to be definitive and absolute. The author mentions a formula (but does not give the name) where the p value can be converted to a probability that the finding was based on a true effect and not simply a false alarm e.g. p-value=0.01 yields only at least a probability 11% or more to be true meaning that there is nearly 89% of a chance that this was based on a false alarm. So, the p-value should not be used alone to interpret statistics but rather needs to be supplemented by at least the confidence intervals and the sample size. Overall, the idea that the p-value truly determines the significance between data samples is false. Instead, the p-value is an indicator that there is a likely significant difference between data samples. In addition, the article suggests that multiple statistical analyses should be done in tandem for a global approach. ]]>

This is an article from psychccentral.com, written by Dr. John Grohol. The article discusses about the misuse of statistics to misrepresent data. In an article, Dr. Grohol wrote about how USA Today used statistics to argue that there is a growing trend for the children living with their children in mid 20’s since 1970s. The author of USA Today suggested that there is a 48% increase of people age 18-34 to live with their parents since 1970. While the number seems significant, Grohol mentioned that this author did not use the data obtained to use in the right context. For instance, the author did not take into account military draft, which would explain lower number of people back in the 70’s comparing to now. Dr. Grohol also reworked the statistics taking into account population growth since 1970s. The result of the percentage of people age 18-34, taken population growth into account, is in fact 16% ( as of 2006), not 48% as author suggested. Additionally, Dr. Grohol also pointed out that 16% increase in the time course of 36 years probably make the data a lot less significant. Thus, it is important for author to tell the full story of their data. The author must interpret data in the right context, also taking into account factors that may make data become inaccurate or biased. On the other hand, the reader also has to be able to see how reliable the story is based on how good and fair the statistical methods of papers are performed and presented.

]]>Errors in statistics can result from a number of bad techniques that often go unnoticed or are assumed to not have any effect on the outcome of studies. These errors in sample data are important to avoid because they can cause misinterpretations of population statistics based on false sample statistics. First, an obvious example of a source of error is to collect data from a sample population that may not be the best represent the hypothesis that you are trying to test. For instance you would not test the prevalence of heart conditions in a younger healthy population. Similarly it is important that all of the variables that may possibly have an effect on the outcome are at least considered if not accounted for. It is also a common source of error to use the same set of data to come up with a question but to also find a solution. The best way to produce valid statistics is to use widespread data that is the best possible/available representation of the population. Although there are other possibilities for error, making sure your sample population is a valid model is your greatest chance for accurate results.

]]>This TED Talk, called “Statistical Errors in Court”, is by Richard Gill, a statistician at Leiden University. He describes the case of Lucia de Berk, a nurse found guilty of several murders based largely on statistical probability of death. Gill is well known for showing the flawed statistical analysis used to make the conviction in the case, which ultimately led to Lucia’s sentence being revoked. The case against Lucia was built on a suspicious pattern: there were nine incidents of death in a medical ward where she worked, and Lucia was present during all of them. In the original conviction of Lucia, a statistical analysis played an explicit role. After the appeal, the verdict was maintained but the flawed statistical calculation based on probabilities was removed. However, the flawed statistical data remained and the “obvious” coincidence between incidents and Lucia’s presence remained a crucial step in the prosecution’s case as well as an influence on the evaluation by medical specialists of the medical evidence. This debate works to confirm the public belief that statistics do not do much credit to real-world situations. The major problem stems from post-hoc testing of hypotheses, which is debated widely, but most participants don’t realize that the data is being used not just to prove Lucia’s guilt but also simultaneously to prove that murders are being committed. This talk also sheds light on the apparent ignorance of probability and statistics in the legal and medical professions, a fact that is often forgotten.

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