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Statistical literacy is a form of literacy and is a term used to describe an individual's or group's ability to understand statistics. Statistical literacy is necessary for citizens to understand material presented in publications such as newspapers, television, and the Internet. Numeracy is a prerequisite to being statistically literate. Being statistically literate is sometimes taken to include having both the ability to critically evaluate statistical material and to appreciate the relevance of statistically based approaches to all aspects of life in general.[1][2][3] H.G. Wells is often cited as saying that statistical understanding will one day be as important as being able to read or write[4] but he may have been referring more to the older idea of political arithmetic than modern statistics.

Aspects of statistical literacy

Many official statistical agencies such as Statistics Canada and the Australian Bureau of Statistics have programs to educate students in schools about the nature of statistics. A project[5] of the International Statistical Institute is the only international organization whose focus is to promote national programs and drives to increase the statistical literacy of all members of society. Numerous resources and activities, as well as a body of international experts help maintain a very successful campaign across the continents. The UNECE has taken the notion of statistical literacy as the subject for its fourth guide to making data meaningful. Recognising the obligation of its royal charter to promote the public understanding of statistics, in 2010 the Royal Statistical Society launched a ten year statistical literacy campaign.[6]

Experiments in the sciences, business models and reports, use statistics. People involved in these fields generally have studied the meaning of statistical quantities, such as averages and standard deviation. Many colleges and universities require an introductory course in statistics as part of a professional program.

Each day people are inundated with statistical information from advertisements ("4 out of 5 dentists recommend"), news reports ("opinion poll show the incumbent leading by four points"), and even general conversation ("half the time I don't know what you're talking about"). Experts and advocates often use numerical claims to bolster their arguments, and statistical literacy is a necessary skill to help one decide what experts mean and which advocates to believe. This is important because statistics can be made to produce misrepresentations of data that may seem valid. The aim of statistical literacy proponents is to improve the public understanding of numbers and figures.

Health decisions are often manifest as statistical decision problems but few doctors or patients are well equipped to engage with these data. [7]

Results of opinion polling are often cited by news organizations, but the quality of such polls varies considerably. Some understanding of the statistical technique of sampling is necessary in order to be able to correctly interpret polling results. Sample sizes may be too small to draw meaningful conclusions, and samples may be biased. The wording of a poll question may introduce a bias, and thus can even be used intentionally to produce a biased result. Good polls use unbiased techniques, with much time and effort being spent in the design of the questions and polling strategy. Statistical literacy is necessary to understand what makes a poll trustworthy and to properly weigh the value of poll results and conclusions.

A problem also occurs with literacy because of the work of statisticians. The legibility of numerical tables is an example given early in the recent book by Richard M. Heiberger & Burt Holland.[8] In their example, rather than an incorrect, confusing, collection of numbers in misaligned columns, the statistician must present results legibly by lining up the decimal points so the visual presentation is more organised.

To be statistically literate, one must also have a solid understanding of probability theory.[citation needed] Studies have shown that human beings’ estimations of probabilities are heavily influenced by context and wording.[citation needed] For example, people typically underestimate the probability of being involved in a car accident because their everyday interaction with vehicles gives the impression that they are safer than they actually are.[citation needed] Likewise, they tend to overestimate the probability of being attacked by a shark because of media or other influences.[citation needed]

Gambling is one setting in which a lack of statistical literacy can be costly.[citation needed] Simple probability theory helps the individual either estimate or calculate the probabilities involved with games of chance. However, most individuals fail to approximate, for example, the probability of being dealt a full-house in a game of poker. Not understanding these probabilities causes the individual to wager more or less than they would knowing at least an estimate of the probability.[citation needed] Increasing individuals’ statistical literacy and knowledge of probability through classroom applications, textbook examples, and other methods, would lead to more informed citizens, capable of making more informed decisions.[citation needed]

See also

External links


  1. Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-920613-9
  2. Wallman, K. (1993) Enhancing statistical literacy: Enriching our society. J. American Statistical Association, 88, 1–8
  3. Gal, I. (2002). Adults’ statistical literacy: Meaning, components, responsibilities (with Discussion). International Statistical Review, 70(1), 1–51.
  4. Wallman, K. (1993) Enhancing statistical literacy: Enriching our society. J. American Statistical Association, 88, 1–8
  5. The International Statistical Literacy Project
  7. Gerd Gigerenzer et al. (2008) "Helping doctors and patients make sense of health statistics" Psychological Science in the Public Interest 8 (2), pp.53-96
  8. Heiberger,R.M., Holland, B. (2004) Statistical Analysis and Data Display. Springer. ISBN 0-387-40270-5


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