Do not let them fool you again with statistics

on   /   in Broken Links 12:35 am   /   Comments

By OMOH GABRIEL

Statistics are very easy ways of presenting complex issues. They can present a graphic situation and possibly make it easy for people to have a snapshot of any given situation. For ease of presentation, statisticians, accountants and economists use data to explain the financial situation of a company, country and region.

economySometimes, cross data and time series data are used to attempt a proper explanation of a given situation. But statistics taken on face value, present half truths and can easily be used by politicians and other vested interests to deceive the public.

The misleading use of statistics to deceive, mislead, and confuse the public is epidemic in both the public and media. Sometimes the statistics are wrong, but just assumed to be correct, like the persistent myth that 50 per cent of marriages end in divorce. Sometimes the individual who uses the statistics uses them sincerely but incorrectly to support an argument that the numbers do not really  support.

The trend is not just a Nigerian affair but global. Even in America that prides itself as the most advanced economy in the world, the use of statistics to achieve selfish political end is common place.

The use of absolute data without proper referencing can be deceptive. Graphs and charts used in some of these statistical presentations also can be misleading to the ordinary persons who do not have a clear understanding of basic statistics.

Every day, the citizenry are bombarded with numbers. Statistics and data provide useful information about the world, but as Nigerians head toward the 2015 elections, Nigerians can expect to see plenty of talk about poll results, margins of error, and the like. Misunderstanding of statistics is also rife, and can lead to a manipulation of the facts that may be either deliberate or inadvertent.

Not only the statistics, but also the way the statistics are presented might deceive you.

Most times, politicians, marketers, accountants and others hide under the use of statistics when they use metrics that sound good at first, but do not actually mean what they are trying to suggest.

For instance when a marketer comes up with a presentation saying 80 per cent of a particular product sold in the last 20 years are still on the road, it is a deceptive statement that the individual needs to evaluate properly to see beyond the argument being presented. The metric suggests “oh yeah, they are really reliable.

If you think hard about it, perhaps 80 per cent of the products sold in the last 20 years were sold within the last 10 years.  This actually does not say much for reliability, does it?

In actual fact, to an economist and those who can reason logically, that would suggest that they do not make it past 10 years. The metric has absolutely no meaning unless you have a lot more information.

Perhaps if they wanted to really show reliability, they would have put it as “80 per cent of all products 20 years or older are still in use.  Now, that would say something about reliability.

And sometimes the statistics are just pure, blatant deceit, designed to mislead by the government and relayed uncritically by news media that are either too eager to support the government or too lazy to apply critical reasoning.

In 1978 for instance, New Nigeria Newspaper presented the Joint Admissions and Matriculation Board result in a front page table suggesting that the then Bendel State candidates for that year’s JAMB admission were more than the entire northern states. This sparked off riot in ABU.

But on a closer examination of the data, the then Bendel State had only about 20 per cent of applicants admitted by JAMB while those who applied and qualified for admission from the north were far less than those from the state.

Very often, politicians, prosecutors, and even scientists are guilty of skewing statistics for their own ends. It’s very tempting to select the data that supports your point of view, especially when careers or funding is at stake.

Keep this in mind, statisticians often do refer to anomalous data as “outliers,” and existence of such may be the result of method bias or built-in systematic. Insurance companies may “cherry-pick” the selection of low-risk, healthier clients that are likely to present a better profit margin.

Another “fruit-based” statistical metaphor arises from comparing apples and oranges, or evaluating two distinct and separate subsets of data as if they are the same.

There are statistics that look convincing, but are often nothing but a façade. Remember those old ads stating, “Cigarette brand X is 90 perr cent smoother than Y?” This is a pure Potemkin number, a logical smoke screen of assigning a specific value to something that can not be measured.

The term comes from an 18th Century legend in Russia, when Prince Gregory Potemkin supposedly used fake villages in Crimea to fool and impress the Empress during her visit. Almost every day, there are news headlines that promote this fallacy. Humans are wonderful at recognising patterns, but this ability can be used against us, often causing us to see connections where none exist.

If statistics were to come out that suggested tennis players suffer higher rates of skin cancer, should we conclude that tennis causes skin cancer? Obviously not, but tennis players do spend more time in the sun, theoretically increasing their skin cancer risk. Indeed, “X causes/cures cancer” is a common version of this fallacy we see in the headlines.

A systematic error typically happens when a hidden bias works its way into the data. For example, take a company that is doing an instant poll of people on the Internet. The pollsters are only getting the slice of the population that is online at the time. Systematic errors are hard to eliminate entirely.

When next you are told that a particular thing is very effective without comparing it to the alternative of doing nothing, or without comparing it to alternatives, just disregard it.

Leaving off information about alternatives is deceptive.  Seriously, it is really easy to be deceived by statistics. What is really hard is not to be deceived.  Seriously, any time someone uses statistics to back up their point, it is likely that there is some kind of deception in there.

 

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