Statistics are an important part of every day life and can be used throughout many different activities. Many statistics, however, are altered and can be deceptive at first glance. One example of this includes the claim that women make less money than men. When analyzing statistics of a set of data, one must take into account sample size and other parameters. In this particular example, it may be true that the mean of women's income is large in a sample of ten participants. However, the mean of men's income may be smaller because a larger sample was taken. Many companies use false statistical data to advertise their product. This claim may be substantiated for a small sample, but not for a whole population. In addition, statistics provide credibility for your evidence or argument. The audience of this article is any person who watches television, reads magazines, or views any form of media. …show more content…
This interests me because it shows how companies use deception to sell a product. Furthermore, I was captivated by the concept of not blinding accepting findings, but instead thinking about the processes, sources, and numbers that prove the concept. This type of thinking is critical in solving problems and generating innovative solutions. In the future, I will use this method of asking questions and using evidence to back up my claims. I found it most difficult to understand the idea of how diverse the usage of statistics in an average day. The idea that probability and other statistical concepts exist in our world through real-life examples fascinates me. This created unrest in me because it is easy to fall into the trap of simply believing what one sees at first glance. This way of thinking could create problematic
• Provide at least two examples or problem situations in which statistics was used or could be used.
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.
When we look around us, we may not recognize that statistics is all around. Before I began to take this course “Statistics for Managers” I was not aware of how statistics actually worked. The first idea that came to my mind about statistics was probability. Not knowing statistics and probability are related because they both determine a possible outcome. Throughout this course I have learned what statistics is and how it works. In this paper, I will describe descriptive and inferential statistics, hypothesis developing and testing, the selection of statistical tests, and how to evaluate statistical results in analyzing data.
Statistics refers to the use of numerical information in everyday life to calculate facts and figures in limitless circumstances. In addition, statistics refers to the scientific collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical data. This week the class’s objectives were to apply the steps in testing a research hypothesis, to compare the means of two or more groups, and to calculate the correlation between two variables. Learning Team D’s members have reflected on each of these issues and share their insights on these objectives.
The major of statistics is used in everyday life whether it be through experiments or analyzing data for a company. With the use of statistics, it is not only important to know how to analyze data, but also how to communicate the information to others. Using the communication skills can help companies sell more products and save money on advertisements. With the advancement of digital and/or electronic communication technology, the field of statistics is growing rapidly and their findings are becoming more accurate. Post graduation I intend on working for a company doing business analytics for the products they sale. In this final paper, I will explain to you how the advancement of digital communication
In the video Don't Be Fooled By Bad Statistics posted by Emily Dressler three forms of bad statistics are discussed, poorly collected data, leading questions, and misuse if center. Information collected poorly will lead to misleading results and false conclusions. Dressler uses the example of data collected by researchers pertaining to magazine preference during business hours. The data is skewed because of the time of day the information was gleaned rendered the sample not representative of the entire population. Another form of bad statistics has to do with how the desired information was elicited. Leading questions may result in biased responses. Questions need to be worded carefully so the information collected is not influenced by the interviewer. Finally, the video talks about misuse of center. Data can be misleading if not appropriately analyzed. Outliers, an individual value that falls outside the overall pattern of data can prejudice the conclusion leading to incorrect assumptions. An example might be that of the man who drowned in a pond with an average dept of one inch. The pond was one quarter inch deep everywhere but in the center where there was a ten foot hole.
Statistics, facts, data, and comparisons are absorbing and challenging to present in a way that is anything other than, well, boring. For purposes of an informational presentation, the statistics are unavoidable. However, in this
1“The Cult of Statistical Significance” was presented at the Joint Statistical Meetings, Washington, DC, August 3rd, 2009, in a contributed session of the Section on Statistical Education. For comments Ziliak thanks many individuals, but especially Sharon Begley, Ronald Gauch, Rebecca Goldin, Danny Kaplan, Jacques Kibambe Ngoie, Sid Schwartz, Tom Siegfried, Arnold Zellner and above all Milo Schield for organizing an eyebrow-raising and standing-room only session.
When people hear anything about "probability and statistics", most people tend to zone out. They will defend themselves by stating that they will never use statistics in their life. For some people this could be the case, but for most people their future may be changed by the use of statistics. A good example of how the use of statistics has changed a group of people’s life is the movie Moneyball. Moneyball incorporates the use of probability and statistics with just a simple game of baseball. It follows the Oakland Athletics general manager,Billy Beane, as he uses the power of statistics in order to create and manage his baseball team. Beane faces a problem when he is unable to re-sign three of his best players due to a limited payroll. He
Statistics involves framing questions in a context, then collecting and analyzing data for interpretation. Probability is about chance and fairness with assigned values. Many mathematics instructors teach this discipline in a procedural manner, causing students to miss its essence.
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.
This article shows more of a political debate and how numbers can be emphasized or left out dependent on one’s ideology. Political agenda governs this debate. These agendas are creating a problem when the data proves
The article does well provide details to support main point of the author argument. In fact, the usage of statistics and other people stories may reinforced the credibility of this article. Furthermore, because of the usage of clear language and key terms that were fine defined aids the readers in understanding the message of the text. The only downfall of the article is some of statistics is not well supported with evidence or a guidelines guiding us back the origins of these statistics, although the paper is not flawless, the overall strong point of it defeats it
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 many people complain about all the math, how boring it is, and how everything about it is awful. This is mostly just seems like it is because they don’t understand. Even Alan Smith reportedly was awful at statistics and computer programming, but his quiz which, changed the perspective of over 250,000 people, managed to change his mind. This is fascinating to me. Although I have never taken statistics, I have a predetermined mindset that statistics is not worth taking. I have always been told to take calculus or linguistics or anything else but statistics, because every other class is easier and has a more defined purpose, especially for my major. No one has ever mentioned the impact that statistics can have on people, however. That is what made this Ted Talk so interesting.