Fooled By The Randomness

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

However, a great deal of conflict was in-part, the result of chance’s influence. Chance is an unknown and unpredictable series of events that causes a certain end result, and it is sometimes

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.

When a state of probability remains undetermined, we often feel compelled to define it. We desire certainty, so we obtain it through whichever way we see fit, even when it is unattainable or harmful to do so. When we imagine

7. Upload your “Concept map” to the Dropbox by the due date provided on the course calendar.

To test just exactly how E.S.P. worked, he created an experiment with Zener cards, which had one of a total 5 pictures on each of them. Rhine would draw a card form the deck and ask the subject to guess what picture was on the card. Out of the many subjects tested, most guessed only about 20% of the cards correct, but one young man averaged about 50% correct. This young man, Adam Linzmayer, would even guess up to 9 cards in a row, which was almost a one in a million chance—he did it three times. Rhine became overly excited of his findings on his belief in E.S.P. and wanted to publish the results. But upon his replication of the experiment, Linzmayer’s success rate of guessing the drawn cards greatly decreased (Lehrer, para. 12-13). This decline effect could possible be due to regressive fallacy, which is the inability to account natural and unavoidable fluctuations in experiments. For example, things like stock market prices and chronic back pain unavoidably fluctuate between prosperous and well-feeling times to poor and pain-filled times. By setting aside the idea of natural and unavoidable fluctuations, one can ultimately fall into self-deception and into post hoc

When we notice random coincidences, we may forget that they are random and instead see them as correlated. We can easily deceive ourselves by seeing what is not there.

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.

Growing up in a family of doctors influenced me to pursue the knowledge of health and diseases. I long for devoting myself to public health. With data explosion, I realized that data-driven science is indispensable to meet the increasing demand in medical and biological improvement. Therefore, I want to apply tools of statistics to address human health problems and become an excellent biostatistician. My goal in entering in the M.S. program at Johns Hopkins University (JHU) is to develop expertise in Biostatistics. At JHU, I am able to obtain an interdisciplinary perspective, combining approaches and knowledge derived from statistics, computing and biology. In the future, I plan to pursue Ph.D. study and one day lead my own research group. To meet these objectives, I wish to carry on advanced study at JHU.

The Signal and the Noise: Why so Many Predictions Fail but Some Don’t talks about how many different statistics are used in everyday life. The book dissects the ways that people try to predict the future by look at past patterns that occur and they try to infer about the future. The book talks about how the statistics that are used by people to infer about the what will occur in the future. The Signal and the Noise does this in a good way because it uses different statistics that many people enjoy or at least know can be useful with topics ranging from sports betting and poker hands to weather and the housing market. It uses these statistics to show how certain times people can predict the future by using statistics from the past but these predictions are not always correct. The book offers many different statistics to show how predictions usually are wrong but can be correct when looking at these statistics under a different light.

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.

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

The Gambler's fallacy, also known as the Monte Carlo fallacy (because its most famous example happened in a Monte Carlo Casino in 1913)[1] . Also referred to as the fallacy of the maturity of chances, which is the belief that if deviations from expected behaviour are observed in repeated independent trials of some random process, future deviations in the opposite direction are then more likely. For example, if a fair coin is tossed repeatedly and tails comes up a larger number of times than is expected, a gambler may incorrectly believe that this means that heads is more likely in future tosses.[2] . Such an expectation could be mistakenly referred to as being due, and it probably arises from

When discussing probability, a text of my previous reading came to mind. Within the lecture Physics II, much of Aristotle’s work is concerned with providing a definition for various events and subjects, and as such, identifying the types of causes for each event is an important step in accomplishing this goal. Aristotle specifically investigates the role of luck and chance as causes of change. Although we commonly speak of luck or chance as being a cause, Aristotle purposefully refrains from including them in his explanation of causes. When giving an account of our observable world, I agree with Aristotle in that there is no place for luck and chance as causes of events, yet I believe they do have a role, namely in predicting future events.

Inspiring What’s Next. >> annual report 2014 t’s ha t. W ex N Maxis Berhad Annual Report 2014 We Are Maxis We are the leading communications service provider in Malaysia, enabling both individuals and businesses to connect and communicate anytime and anywhere.