Applied Statistics


 


Statistics refers to the scientific discipline of data collection, organization and


interpretation, which includes the data collection planning to be used in surveys and


experiments, and is also considered a distinct mathematical science. The field uses


data and statistical models to enable forecasting in such areas as the natural and social


sciences, government, business and sports. Descriptive statistics refers to the summary


of a collection of data that can be used in research, including the modeling of data in a


way that would indicate randomness in observations. Inferential statistics refers to the


use of such random observations to draw inferences on the studied population.[1]


 


Statisticians are specialists in applying statistical analysis. They design experiments and


conduct survey sampling to improve data quality and can help various organizations


without having access to knowledge relevant to their specific problems.[2]


 


Applied statistics refers both to the work of professional statisticians who produce and


present statistics and to statistical interpretation as performed by statisticians, by


statistical users and by the general public. It often use statistics theory to provide a


ready source of information and has contributed significantly to scientific research.


However, applied statistics has also been a source of misinformation, such as in the


statistical misinterpretation by some in the medical, legal and financial professions that


have led to erroneous diagnoses, imprisonment and financial risk assessments.[3]


 


Statistics are numerical observations of facts and therefore fallible. Their reliability


depend on the representability and accuracy of the observed facts and their correct


interpretation. It is also important to appreciate the methods by which statistics are


generated. A controlled experiment is the easiest method to use because disturbing


influences can be minimized, while the design of experiments range of methodologies,


including its associated software packages, is used when there are lesser degrees of


control. Sources of error in taking samples include the variable results every time a


sample is taken and the difference in the qualities of the sample from the population it


intends to represent. Most published and unpublished statistics contain elements of


subjectivity, which is why their objectivity is usually subject to review by an independent


body. The reliability of published statistics are usually only available when a variable is


is obtained from multiple sources or when revisions are made, as in the differences


between victim-reported and police-reported crime rates.[4]


 


Some statistical findings can be successfully interpreted verbally and by a mixture of


verbal logic and the rules of chance. Their successful use involves the combining of


available tools of inference with their safe application to a particular problem.


Managers of such work have the responsibility of understanding statistical concepts,


including their limitations, before making a decision based on them.[5]


 


The quantification of intuitive significance and confidence that would allow objective


answers on questions like how likely is it or how confident are we in relation to a certain


subject are major contributions of statistics theory. Statistical correlation can also be


used to explore a connection when the possible errors in the available evidence are


attributable only to chance.[6]


 


The human brain’s making of intuitive probability judgments is usually erroneous even


if it is fairly right in intuitive judgments such as those involving speed or distance. The


use of heuristics instead of analysis causes distortion in numerical data interpretation,


such as in the overestimation of bigger events like rail disasters and the


underestimation of smaller events like road accidents. Statistical errors such as


misinterpreting non-significant  findings as significant have occurred in medical research


reports in two respected medical journals. Survivor bias has been used by financial


analysts to statistically mislead potential clients to show the benefits that would have


been obtained if investments had previously been made in their currently recommended


portfolio.[7]


[1] “Statistics”, Wikipedia, 4 May 2011, <http://en.wikipedia.org/wiki/Statistics>  [accessed 9 May 2011]


[2] ibid


[3] “Applied Statistics”, Citizendium, 17 February 2011, <http://en.citizendium.org/wiki/Applied_statistics> 


[accessed 9 May 2011]


[4] ibid


[5] ibid


[6] ibid


[7] ibid



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