# How to lie with statistics mobi

How To Lie With Statistics

Sign in. As producers of tables and graphs, we need to effectively present valid summaries. Compared to algorithms or big data processing, data literacy may not seem exciting, but it should form the basis oie any data science education. The classic book discussed in this article addresses responsible consumption of data in a concise, effective, and enjoyable format. When two variables X statjstics Y are iwth — meaning they increase together, decrease together, or one goes up as the other does down — there are four possible explanations:.

X causes Y. Y causes X. A 3rd variable, Z, affects both X and Y. X statistis Y are completely unrelated. We often immediately jump to — or are led to believe — A or B how to trade with cci indicator C or D may be as likely.

For example, when we hear that more years of college education is positively correlated with a higher income, we conclude that additional years of university lead to greater wealth. However, it gow also be a 3rd factor, such as willingness to work hard or parental income, is behind the increase in both more years of tertiary education and hoe income.

The 3rd hidden variable can lead us to incorrect conclusions about causality. Other times two variables may appear to witth correlated, but really have nothing to do with each other. If you make enough comparisons between datasetsyou are bound to find some interesting relationships that look to move in sync. Tyler Vigen documents these at Spurious Dtatistics. Does more praise of students from a teacher lead to higher grades?

Do higher grades cause more praise? Or is there a third factor, smaller how to install python modules sizes or more natural lighting in a class, causing both variables to increase?

Questions of cause are answered by randomized controlled trial s, not by observational studies where we cannot rule out additional factors that we do dith measure. To avoid being misled, approach correlations between variables with skepticism le looking for confounding factors. Linear relationships are almost always only linear in a limited region of both variables. Beyond a point, the relationship how to paint a fiberglass boat at home become logarithmic, completely disappear, or even reverse.

This can be observed in growth statistivs extrapolated over time. There are periods of linearity where growth occurs at a constant rate, but eventually, growth levels off because almost nothing continues growing indefinitely. Extrapolating beyond the region wiyh applicability for a relationship is known as a generalization error. You are taking a local phenomenon and trying to apply it globally. As people rise out of poverty, they tend to become more satisfied with life.

This suggests there are diminishing returns statiwtics increasing wealth, just as there are in many aspects of human activity, like studying for a test. We see extrapolations all the time: syatistics rates for companies, population demographics, prices of stocks, national spending, etc. Remember, relationships in a local area do not always apply globally. Adjusting the axes of a graph to make a point is a classic technique in manipulating charts.

As a first principle, the y-axis how to disable microsoft exchange activesync a bar chart should always start at 0.

Another example of misleading graphs is y-axes with different scales. By carefully adjusting values, you produce surprising trends where none exist. While this may seem like an obvious manipulation, advertisers and newspapers get away with it because people do not read information. Most people see a graph and immediately draw a conclusion from the shape of the lines or bars, exactly as the person who made the graph wants.

To counter this, try mohi axes values. Ho simple examination may tell you changes are not as big as they look and trends have been created from nothing! Once you get some practice making graphs, you realize how easy it is to manipulate them to your advantage. The best protection against inaccurate figures may be firsthand practice in making them yourself.

Would you be surprised if I told you the highest cancer rates tend to occur in the counties with the smallest populations? Not that shocking. How about when What pill looks like tramadol add that the lowest cancer rates also tend to occur in counties with the lowest number of people?

This a verified example of what occurs with small sample sizes: extreme values. Any time researchers conduct a study, they use what is called a sample: a subset of the population meant to represent the entire population. This might work fine when the sample is stahistics enough and has the same distribution of the larger population, but often, because of limited funding or response rates, psychological, behavioral, and medical studies are conducted with small samples, leading to results that are questionable and cannot be reproduced.

Scientists are usually limited to small samples by legitimate problems, but advertisers use small numbers of participants in wiyh favor by conducting many tiny studies, one of which will produce a positive result. Humans are not great at adjusting for sample sizes when evaluating a study which in practice means we treat the results of a person trial the same as a 10 person trial.

A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day, and in the smaller hospital, about 15 babies are born each day. However, the exact percentage varies from day to day. How to beat candy crush saga level 342 hospital do you think recorded more such days?

The larger hospital. The smaller hospital. If you guessed 2. The reasoning is the lir the sample size, the more extreme the values. You can test the principle that small samples produce extreme results by flipping a coin. With a small mohi, say 5 tosses, there how to lie with statistics mobi a good chance you hw 4 tails.

No, this means your sample is too small to draw any significant conclusions. This trick is often used when marketing products by asking statistifs small number of people about a particular brand. Ask a small group, look at the results, throw away the bad, and repeat until satistics get the stats you need! The solution to avoid being fooled by small sample sizes is to just look for the number of observations in the data. If not given, then assume whoever took the study has something to hide and the statistics are worthless.

Checking the sample size can be one way to avoid getting fooled by data, but only if the sample size is provided. Another trick used to mislead consumers of data is to avoid listing relevant numbers that describe a dataset, such as the count of observations, the spread of the data rangethe uncertainty about the data standard errorthe quantiles of the data, and so on. Each of these can be used to get a deeper dive into the data, which often goes against the interest of what cleans soap scum off tile presents the dataset.

For instance, if you hear that the average more on this below temperature in a city is 62 degrees F for the year, that is not helpful without knowing the maximum and minimum. The city could get as cold as F and as warm as F but still average out to a comfortable value. In this case, as in many others, a single number is not adequate to describe a dataset.

As another example from the book, if you have two children, one of whom tests a 99 on IQ and the other ayou really should not tell them to avoid comparisons.

The overall difference might not be significant and could reverse itself in statostics testing. In other words, by leaving out the expected standard error in the results, you can draw a more drastic conclusion than that offered by the data. Think of it this way: if there was a medicine how to calm an aggressive dog down increased lifespan by 2 years on average would you take it?

Would it change your mind if the worst impact was a loss of 12 years of life and the maximum a gain of 14 years? It usually is the details that matter and one summary statistic cannot tell the whole story. This means the mean and median of a dataset are not the same value, often by a considerable amount.

By choosing lis value to report as the average, politicians, marketers, and CEOs can draw opposing conclusions from the same data. The way to avoid this is to look at the mean, median, and mode tl a dataset again you need all these numbers! Figure out which one is how to lie with statistics mobi appropriate usually the median for highly skewed datasets such as income, city size, life span, housing prices and so on and use that if you need a one figure summary.

If you can, graph the entire set of values in a histogram and look at the distribution. Try to use more than a single number to describe a dataset, and if you report an average, specify which wtatistics are using!

When viewing a statistic, the important question often is not what is the value, but how does the current value compare to the previous value? In other words, what is the relative change compared to the absolute magnitude. Data is often on scales with which we are unfamiliar, and we need a comparison to other numbers to know if a statistic represents a real change.

Is a mean radius of km for Mars large? The easiest statlstics to lower unemployment is just to change the definition to exclude statisyics who have stopped looking for work. Changes in the way data is gathered statistcis in the definition how to write abstract and introduction values can often produce statstics results mistaken for actual trends.

To counter this, first, look at the entire series of values for perspective. Second, make sure the definition has not changed over the time range. Only then can you start to draw conclusions from the data series.

You can scare people by saying Statietics York had murders inbut when you compare that to instatisgics realize New York City has never been safer! Remember when we talked about all data being gathered hwo samples which we hope are representative of the population?

In addition to being concerned about sample size, we also need to look for any bias in the sample. This could come from the measurement method used: a landline phone screen might favor wealthier, older participants. Sample bias is particularly prevalent in political polling where showed that sometimes samples are not representative of an entire population.

When examining a study, we need to ask who is being included in the sample atatistics who is being excluded. Samples only included people often college students from Western, Education, Industrialized, Rich, Democratic, Nations. We should also look statistis sampling bias in our sources of information.

How to Lie with Statistics PDF Details

In these eye-opening books, award-winning statistician and author Lee Baker uncovers the key tricks of the trade used by politicians, corporations and other statistical conmen to deceive, hoodwink and otherwise dupe the unwary. Like the pie chart of the UK General Election that showed that the number 34 is smaller than 14, and zero is much bigger than Or that the President of the United States tried to convince us that , is a larger number than , Nice try boys, but we were awake that day!

In his trademark sardonic style, the author reveals the secrets of how the statistical hustlers use numbers, stats, graphs and charts to manipulate and misrepresent for political or commercial gain — and often get away with it. Pirates, cats, Mexican lemons and North Carolina lawyers. Cheese consumption, margarine and drowning by falling out of fishing boats. This book has got it all. Did you know that pirates caused global warming, and that a statistical lie gave rise to one of the fastest growing religions on the planet?

Probably not — you might have missed the memo that day. Did you also know that organic food is the real cause of autism, and that Mexican lemons are a major cause of deaths on American roads?

In this eye-opening book, award winning statistician and author Lee Baker uncovers the key tricks of the trade used by politicians, corporations and other statistical conmen to deceive, hoodwink and otherwise dupe the unwary. Like how the ex-Chancellor of the Exchequer attempted to persuade us that 5 lots of 10 make , or how the President of the United States tried to convince us that , is a larger number than , In his trademark sardonic style, the author reveals the secrets of how the statistical hustlers manipulate and misrepresent data for political or commercial gain — and often get away with it.

Discover the exciting world of statistical cheating and persuasive misdirection. Sarah Palin, abortions, global warming and Usain Bolt. This book has got scandals galore! It must be true — it was on a pie chart broadcast on Fox News. Did you also know that the number 34 is smaller than 14, and zero is much bigger than 22? In this astonishing book, award winning statistician and author Lee Baker uncovers how politicians, the press, corporations and other statistical conmen use graphs and charts to deceive their unwitting audience.

Or how an anti-abortion group made their point by trying to convince us all that , is actually a larger number than , In his trademark sardonic style, the author reveals the secrets of how the statistical hustlers use graphs and charts to manipulate and misrepresent for political or commercial gain — and often get away with it.

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