The value and distortion of statistics

Discussion in 'General Philosophy' started by Maxi, Jan 2, 2006.

  1. Maxi Registered Senior Member

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    Hi! I'm writing an essay on the following topic: "Statistics can be very helpful in providing a powerful interpretation of reality but also can be used to distort our understanding. Discuss some of the ways in which statistics can be used or misused in different Areas of Knowledge to assist and mislead us, and how we can determine whether to accept the statistical evidence that is presented to us."

    Firstly I guess I have to explain a bit the format of how I have to write it. I am a student of the IB (maybe some of you are familiar with it) and it's this essay is on the Theory of Knowledge subject. Basically I have write my ideas and examples with regard to "the ways of knowing" which are: reason, language, perception and emotion; also "areas of knowledge" which are: arts, maths, history, sciences (both natural and social). I might have missed one or so but you get the point.

    Maxime Maison, IB ToK

    “Statistics can be very helpful in providing a powerful interpretation of reality but also can be used to distort our understanding. Discuss some of the ways in which statistics can be used or misused in different Areas of Knowledge to assist and mislead us, and how we can determine whether to accept the statistical evidence that is presented to us.”

    How often don’t we hear in newspapers and people say “according to statistics…” and “statistics show that…”? Firstly one must take note of the definition of statistics: “information based on a study of the number of times something happens or is present”. Statistics is a simplified mathematical language used to present more complex ideas. Perhaps the strength of statistics lies in the simplicity, directness and within context, completeness, of it. It is a form of language that can easily be understood by many. Statistics can be found everywhere; in history they are often displayed to show the number of deaths that occurred in a certain war; in mathematics they are used to see whether a correlation exists between two or several factors; in natural sciences statistics are useful to keep track of the process of experimentation and its development. Statistics are valuable to us because they clarify the complex nature of our society, and help us to perceive what is going on. It is particular helpful when dealing with social matters as they often ask questions that demand statistical answers: who and how many are affected? Is it getting worse? What will it cost to society to deal with it? This type of questions demand evidence to get a convincing answer, and is usually done by statistics. Statistics are perceived differently from person to person and can evoke different types of emotions such as fear depending on how we respond to the statistics. Although statistics can be very valuable in providing an interpretation of reality; its use is not always done properly and can distort us into believing falsified things. Statistics can be seen as a paradox where as stated the simplicity, directness and completeness are its strengths, but these qualities are in addition its weaknesses. Statistics only take in count the numbers of what is studied and does not explain everything that is being measured, so its simplicity can be misleading. The language in statistics is moreover very important for the knowledge that can be derived from it and how it is perceived.

    Statistics have been very useful in scientific progress particularly during the past centuries. To illustrate this with an example take the Edward Jenner’s discovery of vaccination. By comparing statistics of people who died from smallpox, a serious problem, during that period of time; he noticed that there was a correlation between people who had been infected with cowpox and that they had not been infected with smallpox. Thanks to Jenner’s discovery, we can now be vaccinated against certain infectious diseases. Statistics have particularly been useful in the domain of natural sciences as they base on multiple experiments and results derived from those experiments. These results become part of a statistic that are then used into reasoning how the experiment can be altered into achieving the wanted end result. Perception of statistics is what ultimately leads scientist to reason why their results didn’t end up as they wanted. It certainly worked for Jenner, and most scientific progress if not all use this method. In addition to natural sciences statistics are useful in social context to clarify problems that exist, so that we are aware of taking measures to prevent them. An example of a growing problem in todays society is that of AIDS. It is a problem that continues to grow and recent statistics show that 40million people in the world have AIDS, which is approximately 1 out of 150. Looking at these statistics I personally feel frightened as they represent a great number for a lethal disease that so far cannot be cured. This example displays how emotion can affect our perception of the problem of AIDS. People might then be more precautious in the future knowing the risk that exist. Even though 40million is a pretty rough number it can still be representative as to how statistics can be a powerful interpretation of reality.

    We have formerly seen how statistics have been used in the progress of scientific research, as well as making society aware of problems that are occurring. We will now look at the misuse of statistics. Although the simplicity of statistics can be helpful for understanding what is being examined, it often does not tell the whole truth and can therefore be deceptive, and people examining the statistics may jump wrongly into conclusions. In addition statistics can be manipulated so that it demonstrates an idea as wished. For example when doing research for this essay I came across a ToK study guide by Brian King. The website promoting this guide claimed that King’s results were A:35%; B:50%; C: 15% of 80 students. These results are presented so that one will perceive them as impressing. However while these facts are straightforward and simple, there seems to be something missing. Maybe King’s students are all appointed by himself? In that way he can ensure that he is likely to get good results by picking students he knows will perform well. Is the school where King is working a private one? One would then expect that the students attending that school to score overall higher than of a public school due to better circumstances and students would be expected to be more motivated in a private school. The 80 students were they part of a specific year? Or were they only an average of his class size, and the results as well were averages? Do the statistics neglect results from the grades D, E and F as they are not mentioned? The questions raised here are not to question whether King is a good or bad ToK teacher, but to show that in advertising in particular statistics are misleading as they don’t tell the whole truth, and are used only to provoke one type of perception; that of positivity towards the statistics. Here we are led to believe that King is an exemplary ToK teacher because of his results. Is this necessarily true? Say for instance that he was in fact teaching in a private school with motivated students that had excellent study conditions. Compare him now with a ToK teacher (teacher X) from a public school where the conditions were rather poor and the students fairly unmotivated. Say that teacher X got results of A: 15%; B: 30%; C: 55%. If these results were presented without regarding conditions, statistics would show that King is a better ToK teacher than teacher X. Given the whole scope our perception of these statistics are likely to change and we might reason that teacher X is in fact better, as he managed to get quite good results from poor conditions. Moreover our own emotions can mislead us when interpreting statistics. In this case for instance we might reason that teacher X is better because we sympathise with his tough conditions or simply because one attends to a public school oneself. Note also that the way I questioned these statistics was a personal response as I perceived them and reasoned about them. You may have a different view and reasoning to these statistics.

    Furthermore an example where statistics were misused, was in an article from a journal in America. The article claimed that every year from 1950 to 1995 the amount of children gunned down had doubled. One might not realise at first that this statement is preposterous. According to these statistics if the number of children gunned down were in 1950 was 1. Then this would mean that in 1994 the total number would be 35 trillion, which is completely absurd. The statistics the author of the article used were taken from the “Children’s Defense Fund”, and stated “the number of American children killed each year by guns has doubled since 1950”.The wording here is however different, and the language therefore alters our perception of these statistics. The wording here explains that in 1994 there , were twice as many deaths as in 1950, which renders the situation more plausible. If more facts were revealed to these statistics one might find out that during this period the population in America grew with 73%. Yet again explains partially why the figures doubled. But these statistics also raise other issues, such as: Who is considered a child? What do they mean with “killed by guns”? How are these child gunshot deaths counted, and by who? These questions could give the whole interpretation of the statistics another side. For example the Children’s Defense Fund in many of their statistics include everyone under the age of 25. We not only see again how statistics can be misleading because of its simplicity but also how language affect the knowledge that can be derived from these statistics.

    In conclusion we have to think critically when dealing with statistics. We have to ask ourselves question about the statistics examined and reason whether they can be falsified or not. Questions such as: what is its purpose? What’s the source? What is being said and how? Is it biased? Most people just look at the statistics and choose to believe in what they reveal, without questioning. The problem then occurs when they spread the falsified knowledge they’ve just obtained further. The next person then might not question these statistics either and spread the “knowledge” on and so it goes on and on. This is how bad statistics can become general facts. In addition it is also important not to let our emotions affect our perception of the statistics presented as we then misleading ourselves. Statistics is not therefore always the fault of its author but often by the ones interpreting them. One might also ask oneself, are misused statistics necessarily bad? No, because statistics serve different purposes. For instance a government might misuse statistics to emphasize the danger of smoking when it comes to cancer and other health risks. This might cause the amount of smokers to decrease and therefore increase the average health of the population, which is a positive thing. Many statistics are also biased in their purpose of collecting data for example if I wanted to illustrate that policemen in Sweden are bad, all I have to do is ask the people who were protesting against the EU convention in Gothenburg 2001, how they feel policemen are behaving. By choosing a target “audience” for my data I can affect the end result of those statistics. However when I present my data I don’t mention that I specifically chose to ask protestors as they were target for discrimination. Knowing that the majority are likely to give the same answers I can know what the answer will be before even doing the survey.



    mind that it's a first draft done quite quickly but I prefer rushing through things for the first draft because I get most my ideas roughly by doing so. Basically what my teacher told me from the evaluation sheet was that I needed some more examples and more emphasis on "the ways of knowing" and "areas of knowledge". So I'll gladly take some more ideas into consideration and if you somehow think there is something I could improve in the essay (particular sentences, structure, other examples etc) let me know oh yeah for anyone who would like to plagiarise this: don't! You'll just end up getting problems.

    Here's a link if you're intrested in the criteria. http://academy.d20.co.edu/rhs/IB/tok essay strategies.htm

    Thanks on beforehand!
     
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  3. Xerxes asdfghjkl Valued Senior Member

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    Didn't read the whole thing, but I have two points that came up.

    1. IB. Yes, I was in this too. Not diploma, but I knew people who were, and found it to be a bit of a joke.

    2. Best (or most interesting) approach the theory of knowledge is to wander into the realm of nonduality.
     
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  5. duendy Registered Senior Member

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    STATISTICVShey? know whose names come to mind whe yer say 'statistics'...? i'll tell ya,,,BARON MAXES and JB's the resident fascist-racists who delight in inundating us wit 'STATSTICS' which presumably show how superior they and their 'race' is and how totally useless all others are--especially with black skin--in comaparison....out of the terrible-two, j.b is he worst for STATISTICs....whyyy... he's like a sastiticmachiiine!...he eats the fpr breakfast and his pillow material is stastic-sheets

    whats that due you meantion say about the blessed 'IQ tests'? anything?
     
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  7. kriminal99 Registered Senior Member

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    The way I see it statistics are useless except among people who are certain that they share the same biases. Statistics can be easily and stealthily biased to provide the results you want.

    The point is supposed to be to provide "hard evidence" to back up a claim, but because they can be biased so easily it is no different then any other time someone claims something to be the case. Statistics do not stop someone from claiming something based on motives other than sharing information.

    For that matter in many cases its impossible for statistics to be unbiased. To be unbiased you are supposed to sample so that there is no correlation among your sample that might effect the results. Of course you have to use your best reasoning to determine if there is any such correlation, and if it benefits you you would ignore any such correlation.

    Example: Taking a survey on a street corner
    Possible Bias: Persons sampled are representitive of people who frequent nearby shops etc

    Example: Random Telephone Survey
    Possible Bias: Persons sampled are representitive of people home during business hours.

    Example: Any Sampling whatsoever
    Possible Bias: Persons sampled are representitive of people before the survey/experiment was conducted. (Can't sample from the future)

    If you are honestly examing bias you just have to look at the chance that these things will effect the results. If you are purposely biasing an experiment you use these to your advantage, for example a street survey by Big Tobacco taken near a popular coffee house where most people are smokers.
     
  8. leopold Valued Senior Member

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    17,455
    two things can be related to a third but not to each other
    example:
    murder and churches
    the more people a community has the more churches it has
    the more people a community has the more murders it has
    therefor you can assume that churches are the cause of murder
    both murder and churches are directly related to population density but not to each other
     

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