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.
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,
Graphs and statistics offer clear visual representations and quantitative insights, making complex data easier to understand and interpret. They can reveal trends, patterns, and relationships that might not be immediately apparent in raw data. However, graphs and statistics can also be misleading if not presented accurately or if the data is manipulated, leading to misinterpretation. Additionally, they may oversimplify complex issues, glossing over important nuances and context.
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
Statistics can easily be manipulated and used to espouse erroneous or misleading theories.
Look at lots of different sources.Dont rely on one set of statistics.
At the Department of Crime Statistics in South Africa
In an ad for moisturizing lotion, the following claim is made: "…it's the #1 dermatologist recommended brand "what is misleading about the claim?
If your null hypothesis is not correct. If the results you achieve are not able to be replicated and/or the margin of error is too great. If you do not have all the necessary and sufficient variables, and controlled for them, and/or they are not relevant. Monty Python had on one of there Flying Circus shows an excellent demonstration of the problems of statics. It showed a segment with large Penguins, then small Penguins as subjects and first small fish and then large fish as rewards and finished with the substitution of BBC programmers as subjects. I wish I had saved the copy I used in class or could remember the episode.
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,
they are graphs that are misleading
This is a misleading answer: 2 + 2 = 17 & threequarters....Misleading means information that is knowingly incorrect.
The phrase "Lies, damned lies, and statistics" suggests that statistics can be manipulated to mislead or distort the truth, implying that there are three categories of deception: straightforward lies, more egregious lies, and the misleading use of statistics. While not a formal categorization, this expression highlights the idea that statistics can be as deceptive as outright lies when presented without context or clarity. Ultimately, it serves as a caution against accepting statistical claims at face value without critical examination.
Misleading is an adjective.