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There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.[20] A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie With Statistics[20] outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).[21] Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.[22] Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias.[23] Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs.[22] Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented.[23] To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole.[24] According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism."[25] To assist in the understanding of statistics Huff proposed a series of questions to be asked in each case:[25] Who says so? (Does he/she have an axe to grind?) How does he/she know? (Does he/she have the resources to know the facts?) What’s missing? (Does he/she give us a complete picture?) Did someone change the subject? (Does he/she offer us the right answer to the wrong problem?) Does it make sense? (Is his/her conclusion logical and consistent with what we already know?) |
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