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|The concept of error is central throughout all statistical work. Whenever there is a measurement http://www.teammlbdiamondbacksshop.com/authentic-curt-schilling-diamondbacks-jersey/ , inference or decision making, the possibility of error is present. So, we have to deal with errors of measurement, errors of inference and decision making.
It is useful to distinguish the two types of errors that may be present in statistical measurements http://www.teammlbdiamondbacksshop.com/authentic-chris-owings-diamondbacks-jersey/ , namely, systematic errors and random errors. Systematic errors are also called as non sampling errors and the random errors are also called sampling errors. The systematic errors as the term implies, causes a measurement to be incorrect in some systematic way. They are the errors involved in the procedures of a statistical investigation and may occur in the planning stages or during or after the collection process. Examples of cause of systematic error (or bias) are faulty design of a questionnaire (such as misleading or ambiguous questions) systematic mistakes in planning and carrying out the collection and processing of the data, non response and refusals by respondents to provide information and a great discrepancy between the sampling frame and the target Universe. If observations have arisen from a sample drawn from a statistical universe http://www.teammlbdiamondbacksshop.com/authentic-archie-bradley-diamondbacks-jersey/ , systematic errors are those that persist even when the sampling size is increased. As a generalization, these errors may be viewed as arising primarily from inaccuracies or deficiencies in the measuring instrument.
On the other hand, random errors or sampling errors may be viewed as arising from the operation of a large number of uncontrolled factors, conveniently described by the term: chance. As an example of this type of error http://www.teammlbdiamondbacksshop.com/authentic-aj-pollock-diamondbacks-jersey/ , if repeated random samples of the same size are drawn from a statistical universe (with replacement of each sample after it is drawn) than a particular statistic, such as the arithmetic mean, will differ from sample to sample. These sample menus tend to distribute themselves below and above the true population parameter (arithmetic mean), with small deviations between the statistics and the parameter occurring relatively frequently and large deviations occurring relatively infrequently.
The word: “true” has quotation marks around it because it refers to the figure that would have been obtained through deviations occurring relatively infrequently. The word: “true” has quotation marks around it because it refers to the figure that would have been obtained through complete coverage of the universe. The difference between the mean of a particular sample and the population mean is said to be random error (or sampling error). The complete collection of factors that could explain why the sample mean differed from the population mean is unknown. http://www.teammlbdiamondbacksshop.com/hats/ , but we can conveniently sum them up altogether and refer to the difference as a random or chance error.
Random errors are those that arise from differences found between the outcomes of trials (or samples) and the corresponding universe value using the same measurement procedures and instruments. The sizes of the differences indicate the reliability or precision. Random errors decrease on the average as sample size increases. It is precisely for this reason that people prefer a large sample of observations to a small one, all other things being equal; that is, since sampling errors are on the average smaller for large samples, the results are more reliable or more precise.
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