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Examples of How to Lie with Statistics

Many governments, political activists, and others with a hidden agenda know how to lie, and do so regularly. Lying with statistics is one of the most common tactics. What I mean by this is that they can use numbers that are perfectly accurate, yet use them in a way that is meant to deceive or to purposely slant people's perspective in ways that are not noticeable to most. That is often the same as a lie in my opinion. Now let's look at some examples.

Non-Random Sampling

For statistics to be meaningful, the sample selected has to be random. When polls were done during one of Franklin D. Roosevelt's presidential campaigns, they showed that he would lose the race by a significant margin, but he won. The problem was that the polls were conducted by telephone, and only relatively wealthy people had phones at that time. In other words, there was a selection of one group of people rather than a true random sampling. That group made up a small percentage of the population, but the people in it were more likely to vote for the other candidate, and they were the ones polled. It was probably an honest mistake, but the principle has been learned and put to use more consciously since then.

In more recent times the best examples of non-random sampling which is meant to produce a perspective or at least known to do so and used anyhow, is found on television polls that require viewers to call in and express their opinion. On a given issue, 80% of the population may feel a certain way, yet not as strongly as the 20% who hold the opposite viewpoint. Of course the latter are more likely to participate in such polls. The producers of these shows certainly know this in many cases, but go ahead with their plans with only the slightest acknowledgement of the non-scientific nature of the process.

In addition to this there is the composition of the audience to consider. More conservatives watch some networks while more liberals watch others, so the same poll questions would get different responses depending on which network asks them. Granted, the networks do mention in passing that these are not scientific polls, but then if they have no validity, what's the point? It's a reasonable conjecture that the purpose is often to promote a particular agenda by showing support for it.

Up To...

I recently read that, "Those who smoke have up to 10 times more indoor air pollution." In other words, the average might be 1.2 times more, but one person in the study had 10 times more. This is how to lie with statistics by playing loose with the particular examples. It gives no meaningful information about the average level of additional pollution that comes from smoking. Watch out for "up to" in any statistics.

Correlation and Causation

Perhaps the most common way to misrepresent an issue is by presenting correlation as though it equals causation. For example, some point out that citizens in the United States have more guns than this or that country or group of countries, and more gun-related crime. The implication is that guns cause more crime, but the statistics alone don't show that. A correlation does not prove causation.

In fact, if we were to compare Switzerland, which has far less gun-crime than the U.S. and yet essentially issues a gun to every adult, we find the opposite correlation. That doesn't prove that guns reduce crime, although it does provide some evidence which argues against the proposition that guns cause crime.

Data Selection

In addition to biased sampling, there is also the problem of biased selection of data. To be truly scientific, when research is done all trials should be accounted for in the results, but because there is no research trial registry in the United States, pharmaceutical companies have been caught discarding trials that show a drug had no effect, in order to make an effect "appear" in the trials they select for the purposes of gaining approval of a drug. If half your trials show a beneficial effect and half show no effect or even a negative one, just throw out the latter trials and you have a seemingly successful product based on the statistical evidence - the carefully selected evidence anyhow.

Or how about counting only those who are getting unemployment benefits as unemployed? This is the routine way government present statistics that supposedly show how many people are out of work. Does it make any sense to exclude those who are no longer receiving unemployment benefits and still don't have a job from the numbers? It is hard to imagine any purpose other than deception.

These are just a few of the many ways to lie with numbers and statistics. Most of them are missed by the average consumer of news or government reports.


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