Lying with statistics, or the use of statistical manipulations to mislead or deceive, is a common and dangerous tactic that can be used to manipulate public opinion, sway decisions, and advance personal or political agendas. It is important to be aware of the ways in which statistics can be manipulated and to approach all statistical claims with a critical eye.
There are several ways in which statistics can be used to lie. One way is through the selection of only certain data that support a particular argument, while ignoring or dismissing data that do not. This is known as cherry-picking. For example, a politician might present data showing the success of a policy while ignoring data that shows its negative consequences.
Another way to lie with statistics is through the manipulation of the scale or axis on a graph. By changing the scale or axis, the appearance of the data can be distorted to support a particular argument. For example, a graph may show a dramatic increase in the number of people using a certain product, but if the scale is not clearly labeled or is manipulated, the increase may not be as significant as it appears.
A third way to lie with statistics is through the use of misleading averages, such as the mean or median. Averages can be misleading because they do not take into account the distribution of the data. For example, if the data consists of a few very high values and a large number of low values, the average may not accurately reflect the majority of the data.
It is also important to be aware of statistical terms that may be used to mislead or confuse, such as "significantly" or "statistically significant." These terms do not necessarily mean that the results are important or meaningful, but rather that the results are unlikely to have occurred by chance.
In order to avoid being deceived by lies with statistics, it is important to be critical of all statistical claims and to examine the data and methods used to generate the results. It is also helpful to consult with experts or seek out additional sources of information to verify the accuracy of the claims. By being informed and vigilant, we can protect ourselves from being deceived by lies with statistics and make more informed decisions.
How to Lie with Statistics Notes (Chapter 3)
In doing so, he demonstrates just how little relevance GDP has to moral principles such as equity, social justice and redistribution, and shows that an alternative is possible, as evinced by the 'de-growth' movement and initiatives such as transition towns. It gets by only because the magic of numbers brings about a suspension of common sense. So the first deviation is 10 90 to 100 , the second deviation is 0 100 to 100 , and the third 100 to 110 is 10. Beginners in health research have to consult different sources if they want to get a complete grasp of this whole spectrum of the research process. Lastly, you should watch out for a switch on the subject. Huff uses a survey of readership of two magazines, which had refuting results. It is usually assumed that the author of the statistic is knowledgeable in the field to which the statistic pertains.
How To Lie With Statistics Summary Essay Example
Only when there is a substantial number of trials involved is the law of averages a useful description or prediction. The author talks about percentages in this chapter, where growth can be measured in percentages or percentage points. We also re-organize books for clarity, putting the most important principles first, so you can learn faster. The source may not be obvious, because liars often borrow the numbers of reputable organizations, such as universities or labs, but come to their own conclusions. In practice, random samples rarely exist. Each kind is calculated differently and gives different information and a different impression about the data: Average Type 1: Mean. Have you ever wondered how the people who publish statistics so often turn up numbers that support their position? While some of these facts and figures may be valid, often they are incorrect or misleading ways to convince us of the agenda of some person or organization.
How to Lie with Statistics by Darrell Huff
Flakes Example- a The graph had no numbers on one axis, so the increase in earnings could have been 2 million, or just 2 dollars 1. Even if there are just a few high salaries, the math. If, however, it fails in either respect it may be far less accurate than an intelligent guess and have nothing to recommend it except a spurious air of scientific precision. Christmas Gift- a Buy a gift now and save 100%. They offer perspective on the past and make the future predictable or, at least, feel that way. Imagine you have a barrel of beans, some red and some white, and you want to find out exactly how many of each color you have. In How to Lie With Statistics, author Darrell Huff explains how people who want to conceal the truth manipulate numbers to come up with statistics that support their positions.
How to Lie With Statistics Book Summary
The first thing you can do is ask the right questions. However, getting a truly representative sample is almost impossible for time, money, and other practical reasons. Therefore, most infants hate living in homes with green wallpaper. Psychiatrist Example- a Said that everyone is neurotic, but the type of person to go to a psychiatrist already isn't "normal" and doesn't represent the population of people 6. Respondents were asked which magazine they read the most - Harpers or True love story. As you learned in the first chapter, a sample is only considered good if it is a good representation of the whole. They then put this over the percentages of increase graph, whose line was flat and showed little increase.
Summary of How to Lie With Statistics by Darrell Huff
That is because there are three common kinds of average: mean, median, and mode. If not, it may be inaccurate or biased. Most respondents came back that they read the True Love Story, but that publisher's figures came back that the True Love Story had a much higher circulation than Harpers - refuting the results from the sampling. This is much less likely to happen in a sample of, say, 120 people. What Is Meant By Lying With Statistics? It is up to you to stay informed and understand how others will misuse statistics to deceive you. Both factors may be effects of some other cause. Report confidence intervals as well as P-values.
