Almost all statistics can be misleading depending on how they are presented and reported.
There is always a margin of error, for example the sample size used to generate the statistics.
For example:
If I ask two random people on the street if they drink beer. I may get the following response: neither drink it, one person drinks, both drink it.
I cannot quite rightly say that this is a true reflection of the population, as I have only sampled two people. Also what about the sex of the people? Men are more inclined to drink beer than women. What about the age of the two people I sampled? If they were minors they may not drink at all (or possibly they do). If I were to only ask men, this would show biase in my sampling.
Therefore the figures need to be presented in context, also a representative sample size needs to be taken - the larger the sample the more accurate the results (or higher the confidence).
Another concept is that statistics are not necessarily mutually exclusive.
Again I ask 10 people on the street: Do you drink Whisky or Beer?
If 6 people answer positive for drinking whisky, this does not necessarily mean that the other 4 people automatically drink beer. They may not drink at all, or they may drink both.
Statistics are useful if interpreted correctly, but they should always be presented in context.
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Statistics themselves are purely factual and can not be biased or misleading. When people start making inferences and interpretations based on the statistics, that is when they can become biased or misleading.
The world is littered with statistics, and the average person is bombarded with five statistics a day1. Statistics can be misleading and sometimes deliberately distorting. There are three kinds of commonly recognised untruths: "Lies, damn lies and statistics." - Mark Twain
At the Department of Crime Statistics in South Africa
Lies or damned lies! These would be statistics which are faulty or presented in a misleading way (deliberately or accidentally). Such statistics could arise in a number of ways:the experimental model was flawed,there were errors in measurement or recording,the sample was biased,correlation was interpreted as causation,poor graph design - scales, pictograms using improper dimensions,
The term misleading is the number that does not seem in pattern of the others.