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  1. An error is a bound on the precision and accuracy of the result of a measurement.
  2. The error is a particularly prevalent in data mining and machine learning.
  3. An error is a difference between desired and actual performance.
  4. An error is a deviation from a correct value caused by a malfunction in a system or a functional unit.
  5. An error is a difference between a computed, estimated, or measured value and the true, specified, or theoretically correct value.


  1. Engineers often seek to design systems in such a way as to mitigate or preferably avoid the effects of error, whether unintentional or not.
  2. The word error has different meanings in different domains.
  3. In telecommunications, an error is a deviation from a correct value caused by a malfunction in a system or a functional unit.
  4. See also errors and residuals in statistics; the difference between an error and a residual is also dealt with below.
  5. An error is said to occur when perfect fidelity is lost in the copying of information.

Standard Error

  1. The standard error (.016 or 1.6%) helps to give a sense of the accuracy of Kerry's estimated percentage (47%).
  2. The critical value 8 is one standard error higher than the population mean 4.
  3. Standard error measures the variability of sample means.
  4. Since we do not know the population proportion, we cannot compute the standard deviation; instead, we compute the standard error.

Confidence Interval

  1. The standard error can be used to create a confidence interval within which the "true" percentage should be to a certain level of confidence.
  2. For normal distributions, the confidence interval radii are proportional to the standard error.
  3. The margin of error has been described as an "absolute" quantity, equal to a confidence interval radius for the statistic.

Maximum Margin of Error

  1. The margin of error for a particular individual percentage will usually be smaller than the maximum margin of error quoted for the survey.
  2. In media reports of poll results, the term usually refers to the maximum margin of error for any percentage from that poll.
  3. For a simple random sample from a large population, the maximum margin of error is a simple re-expression of the sample size n.

Systematic Error

  1. If the cause of the systematic error can be identified, then it can usually be eliminated.
  2. These can be classified into two types: statistical error (see above) and systematic error.
  3. Obtain estimates of systematic error at important medical decision concentrations.

Sampling Error

  1. The margin of error is a statistic expressing the amount of random sampling error in a survey 's results.
  2. If the exact confidence intervals are used, then the margin of error takes into account both sampling error and non-sampling error.
  3. These are typical sources of response bias.Sampling error is well-defined for probability samples.
  4. Note: The term sampling error refers only to random errors, not errors created by a bias.

Type Error

  1. A type I error is when the conclusion was made in favor of the alternate hypothesis, when the null hypothesis was really true.
  2. The smaller the type I error rate is, the less the chance of making an incorrect decision, but the higher the chance of having to reserve judgment.
  3. The probability of rejecting the null hypothesis if it is true is called the Type I error rate of the statistical test and is typically denoted as .

Confidence Level

  1. The correct answer is (E). The confidence level is not affected by the margin of error.
  2. This interval assumes there is only sampling error, a function of the confidence level, the population standard deviation, and the sample size.

Generalization Error

  1. To obtain an intuitive understanding of Shao (1993), let's review some background material on generalization error.
  2. Early stopping chooses a point along this path that optimizes an estimate of the generalization error computed from the validation set.
  3. The generalization error of a function approximator, feature set or smoother can be estimated directly by the leave-one-out cross-validation error.


  1. In other words, the maximum margin of error is the radius of a 95% confidence interval for a reported percentage of 50%.
  2. This level is the probability that a margin of error around the reported percentage would include the "true" percentage.
  3. This maximum only applies when the observed percentage is 50%, and the margin of error shrinks as the percentage approaches the extremes of 0% or 100%.
  4. To conclude, the margin of error is the 99 percent confidence interval for a reported percentage of 50 percent.


  1. In software engineering, the term error refers to an incorrect action or calculation performed by software.
  2. In more general parlance, the term error is also used to describe incorrect actions on the part of a programmer.

Error Term

  1. Arrows also connect the error terms with their respective endogenous variables.
  2. Mean population error is zero: The mean of the (population) error term (see above) should be zero.
  3. Such measurement error terms represent causes of variance due to unmeasured variables as well as random measurement error.


  1. Pollsters acknowledge this uncertainty by providing margins of error for their polls.
  2. To do that, the pollster needs to have enough women, for example, in the overall sample to ensure a reasonable margin or error among just the women.

Reported Margin of Error

  1. This calculation gives a margin of error of 3% for the Newsweek poll, which reported a margin of error of 4%.
  2. A larger sample size produces a smaller margin of error, all else remaining equal.
  3. The reported margin of error is the margin of "sampling error".
  4. The range of the confidence interval is defined by the sample statistic + margin of error.
  5. For example, let 36% be the number of people in the poll decided to vote candidate A and the margin of error for the survey be 3%.

False Positive

  1. In the RS situation, a Type I error represents, in a sense, a "false positive" for the researcher's theory.
  2. Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives.

Syntax Error

  1. A syntax error is an ungrammatical or nonsensical statement in a program; one that cannot be parsed by the language implementation.
  2. The syntax error is easiest to detect since the code will not compile properly and cannot be parsed.

Hypothesis When

  1. Type I error: " rejecting the null hypothesis when it is true ".
  2. Type II error: " accepting the null hypothesis when it is false ".

Mean Square

  1. A test of lack-of-fit can then be performed, using the mean square pure error as the error term.
  2. It is entirely appropriate to use alternatives to the mean square residual as error terms for testing hypotheses in such designs.

Mean Squared Error

  1. One technique minimizes the mean absolute error, or some other function of the residuals, instead of mean squared error as in linear regression.
  2. Root mean square error (RMSE). The RMSE of an an estimator of a parameter is the square-root of the mean squared error (MSE) of the estimator.

Error Mean

  1. The standard deviation of the sampling distribution of the mean is called the standard error of the mean.
  2. This t statistic is the difference between the sample mean and the hypothesized mean divided by the estimated standard error of the mean.


  1. The mean absolute percent prediction error (MAPE), .
  2. In other words, this is the error of accepting an alternative hypothesis (the real hypothesis of interest) when the results can be attributed to chance.
  3. The mean_squared_prediction_error, MSE, calculated from the one-step-ahead forecasts.
  4. The mean percent prediction error, .The summation ignores observations where y t = 0.
  5. Any sampling distribution used in estimating a population parameter has a mean and a standard error.


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  Originally created: August 16, 2007.
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