Statistics Fool People Statistics

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.

For this week's lecture, Dr. David Laude focused on the reasons why people often hate statistics by giving his input on how it allows for the better understanding and evaluation of data collected. By illustrating different ways in which people use statistics to reach a conclusion, the professor was able to highlight the way these influence decision-making scenarios. To establish his point, the professor purposely explained statistics with the use of scenarios that would make students attentive on the subject. like the ways in which statistics measures a student’s capabilities to perform in the university.

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Use the given degree of confidence and sample data to construct a confidence interval for the population proportion. 1) Of 346 items tested, 12 are found to be defective. Construct a 98% confidence interval for the percentage of all such items that are defective. A) (3.34%, 3.59%) B) (1.18%, 5.76%) C) (1.85%, 5.09%) D) (0.93%, 6.00%) E) (0.13%, 6.80%) 2) Of 81 adults selected randomly from one town, 64 have health insurance. Construct a 90% confidence interval for the percentage of all adults in the town who have health insurance. A) (71.6%, 86.5%) B) (67.4%, 90.7%) C) (70.1%, 87.9%) D)

The speaker for the Ted talk series on fooling juries first talks about the chances of a result from a coin toss is a specific order. The speaker presents two different patterns of the coin toss, one set as HTH and the second as HTT. The speaker then proposes three different answers, first that the average number of tosses until HTH is larger than the average number of tosses until HTT. The second possible answer is that the average number of tosses until HTH is the same as the average number of tosses until HTT. The third is that the average number of tosses until HTH is smaller than the average number of tosses until HTT. Just bases off of the information given and without attempting to solve the question, the most logical answer that I selected was B, being that the average

This article addressed the topic about how many book that American people read. Based on this article, the authors reported that US people read only a single book with 12 months; however, the statistic shows an increasing trends in reading the digital books.

These results are said to be noteworthy at a likelihood of p = 0.05. This implies just 5% of the time would you hope to see comparable information if the invalid theory were right; subsequently you are 95% certain that information does not fit a 3:1

of Statement 9.3 and another statement; we add probabilities. Even if the evidence makes it very

Faulty statistics seem to create a life of their own, they linger around. For scholarly reasons, a graduate student had come across and used a cited, nevertheless negative social statistic. This led to the questioning of where did this statistic originate from? There were no questions or critical thinking when this incorrect idea was not only formed, yet publicized. Unfortunately, society is likely to welcome and reproduce bad statistics, whether they be created or guessed, they could alter the knowledge of humanity. Data is needed though utmost importantly, need to be reputable for us to correctly and informative. Presenting many examples, explaining why statistics are essential, who uses them, and why, the writer provides somewhat of a list of considerations to make when one is approaching them.

I agree with Heather Donald’s article more because he seemed to use more statistics and facts rather than emotional reasoning. While I completely agree with a lot of the statements in Charle Blow’s article, using past history as the main crux of your argument without contemporar statistics or actual facts to back it up make it less reliable. Of course racism should be combated; but, the real question is how much evidence is there that actually shows the amount of explicit racism within our society. Another question is the idea that we are focusing on the we may be focusing on the wrong culprit. Statistically speaking, increased poverty directly correlates to increased crime and African-Americans are more likely to be in poverty than their White

Vice versa, the less time spent and page viewed, the less payment made. The total amount of transaction have reached two peak points recorded $758.86 on Monday and $925.43 on Friday. From Tuesday to Thursday, has a slight decline from $414.86 to $294.03. Furthermore, on both Saturday and Sunday, the transactions have a relative low amount compared to the rest of the days, recorded $394.4 and $222.15, respectively.

I agree with you, not all statistics can be trustworthy. We need more than one source to look out for information of any kind. About having a large range of data, Businesses use statistics to forecast sales, production, etc. they also estimate when and how much they need to increase the price of their products in order to maximize profit. According to Mirabella (2011), a sample size of 30 or more would be sufficed to any statistical test (Mirabella, 2011).

The Census Bureau data collected in the ACS (American Community Survey) is an ongoing statistical survey that provides data every year to communities for future investments and services. The information from the inquiry also generates data that help determine how more than $400 billion in federal and state funds are distributed each year (About the American Community Survey, n.d). The (ACS) data given for each geographic area are presented together with margins of error at factfinder2.census.gov. Furthermore, the data are period estimates, that is, they represent the characteristics of the population over a particular 60-month data collection period. Since answers to income questions are frequently based on memory and not on records, many people tended to forget minor or sporadic sources of revenue and, therefore, underreport their income. Underreporting

The base rate fallacy describes cases where people misjudge the likelihood of an event because they do not should account for other information besides base rate. In this case, people would say that there is 90% percent chance that we get a patient has breast cancer given the test is positive. However they ignore the probability 0.8% , the base rate for breast cancer in our

However, this is not correct, and is a manifestation of the gambler's fallacy; the event of 5 heads in a row and the event of "first 4 heads, then a tails" are equally likely, each having probability 1⁄32. Given the first four rolls turn up heads, the probability that the next toss is a head is in fact, . While a run of five heads is only 1⁄32 = 0.03125, it is only that before the coin is first tossed. After the first four tosses the results are no longer unknown, so their probabilities are 1. Reasoning that it is more likely that the next toss will be a tail than a head due to the past tosses, that a run of luck in the past somehow influences the odds in the future, is the fallacy.

2. We have assumed that there are only two possible outcomes if the test is imperfect i.e. whatever the system turns out to be (bad or good)