Darrell Huff How To Lie With Statistics Summary

Statistics provides us with very useful tools and techniques that aide us in dealing with real world scenarios. I have been able to learn several useful concepts by studying statistics that can aide me in making rational and informed decisions that are supported by the analysis results. Statistics as a discipline is the application and development of various processes put in place to gather, interpret, and analyse the information. The quantification of biological, social, and scientific phenomenons, design and analysis of experiments and surveys, and application of

Although some statistical evidence given is not backed with proper citations, the reader can find that the evidence given is effective in proving her point.

In the video "How Statistics Fool Juries," Oxford mathematician Peter Donnelly attempts to demonstrate through a number of examples how statistics, when viewed in a common manner, can be misunderstood and how this can have legal repercussions. Through a number of thought experiments, Donnelly provides the audience with examples of how seemingly simple statistics can be misinterpreted and how many more variables must be taken into account when calculating chance. Primarily he exposes the audience to the concept of relative difference, or the difference in likelihood between two possibilities in the same scenario. He then goes on to explain that without an understanding of this concept, many juries misunderstand statistics used in trials and very often convict people based on this faulty understanding.

• Provide at least two examples or problem situations in which statistics was used or could be used.

Prior to this course I have used statistics in my professional life. During my previous employment at the University of Tennessee, College of Veterinary Medicine I worked in the student admissions which made my primary job focus the student application process. This process required me to compile statistical information on our applicant pool for faculty and administrative staff to determine who would be accepted. This statistical data included both categorical data, such as current grade level in undergraduate studies, gender, and ethnicity, as well as numerical data such as age, GPAs, average number of courses taken in a semester, credit hours completed, and test scores (Mirabella, 2011). After the admissions process was completed, these different

3. According to the authors, what are the “three simple steps to doing Statistics right?” 4. What

the audience, and it is hard to put it to perspective. Therefore, a statistic is appealing to the

Chapter three of Freakonomics by Steven Levitt lays out an argument against the population’s capacity to hastily believe conventional wisdom. Commencing the chapter with anecdotes about faulty statistics and facts provided by so called “experts,” Levitt sews a seed of suspicion in the reader’s mind. Citing anyone from homelessness experts, to women's rights activists, to police departments, Levitt walks the reader through erroneous proclamations by individuals who drive the common knowledge of everyday people. After introducing each fabricated fact, Levitt not only invalidates each one respectively, but also goes on to explain why each expert provided such bogus information. To summarize Levitt’s commentary, each expert holds different motivations

Mona Chalabi spoke at a ted talk convention about three ways to spot a bad stat to an audience of young adults in February 2017. She spoke about the bad nature of Statistics. Through a convincing Ted Talk “Three ways to spot a bad stat” Mona Chalabi informs the public about the dishonesty of modern day stats.

In his 2013 book, Naked Statistics, Charles Wheelan explains a field that is commonly seen, commonly applied, and commonly misinterpreted: statistics. Though statistical data is ubiquitous in daily life, valid statistical conclusions are not. Wheelan reveals that when data analysis is flawed or incomplete, faulty conclusions abound. Wheelan’s work uncovers statistics’ unscrupulous potential, but also makes a key distinction between deliberate misuse and careless misreading. However, his analysis is less successful in distinguishing common sense from poor judgement, a gap that enables the very statistical issues he describes to perpetuate themselves.

Source: G. C. Britz, D. W. Emerling, L. B. Hare, R. W. Hoerl, & J. E. Shade. "How to Teach Others to Apply Statistical Thinking." Quality Progress (June 1997): 67--80.

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

In How to Lie with Statistics (Huff, 1954), Darrel Huff deciphers statistical examples and explains the means of deception that statistics and statisticians sometimes use to relay false information. Huff also conveys an underlying message of don’t believe everything you’re told, something him and my mother have in common. At first glance, a reader might think that this book will teach people how to actually lie using statistics, but that is not the case. It gives the reader a glimpse or a behind the curtain view of how easily it is to be deceived using numbers and how it is slyly achieved. Ironically he calls the book How to Lie with Statistics almost to tease his audience that the content in this book is not as it appears. To my utmost surprise, I actually rather enjoyed this book. It was a fairly simple read that was filled with new information and showed me how to look closer at statistical figures in the future. The humor was spot on so much, so that I even chuckled aloud occasionally. For the icing on the cake, I even expanded my vocabulary to learn fun words such as rotogravure.

The statistics did this, because it brought more attention to the severity of the problem. A statistic that Farley and Sykes used to strengthen their argument was “Only one in six low- income ZIP codes has a supermarket, compared with one in two high- income ZIP codes” (A19). This shows that five out of six low-income areas do not have a supermarket. The reader also emotionally connects with this statistic, because it is hard to ignore since it is said so simply. Another statistic that is used is, “140 feet of shelf space for fresh fruits and the same for vegetables, but small stores had on average a pitiful three feet for fresh fruits and six for fresh vegetables” (Farley and Sykes, A19). People also emotionally connect to this statistic because they can visualize what size the shelfs are and how little space there truly is for healthy food. Both of these statistics show how the reader can visualize the facts and how they appeal to readers’ emotions. Even though the statistics are logical, Farley and Sykes use them

So while I never want to take a statistics course, and while statistics scientifically involves so many numbers and mathematical principles, I am now interested in seeing how statistics is different from what everyone has said. It is wonderful to think that everyone can be connected through these