error

Examples of error in the following topics:

• Overview of Statement Changes and Errors

  • Despite best efforts, occasionally an error is made on the financial statement and must be corrected.
  • Despite best efforts, occasionally an error is made on the financial statement.
  • Please note: an error correction is the correction of an error in previously issued financial statement; it is not an accounting change.
  • A counterbalancing error has occurred when an error is made that cancels out another error.
  • If the error has not counterbalanced then an entry must be made to retained earnings.

• Chance Error

  • Random, or chance, errors are errors that are a combination of results both higher and lower than the desired measurement.
  • While conducting measurements in experiments, there are generally two different types of errors: random (or chance) errors and systematic (or biased) errors.
  • A random error makes the measured value both smaller and larger than the true value; they are errors of precision.
  • In this case, there is more systematic error than random error.
  • In this case, there is more random error than systematic error.

• Impact of Measurement Error

  • Measurement error leads to systematic errors in replenishment and inaccurate financial statements.
  • Measurement error is the difference between the true value of a quantity and the value obtained by measurement.
  • The two main types of error are random errors and systematic errors.
  • In sum, systematic measurement error can lead to errors in replenishment.
  • As a result, an incorrect inventory balance causes an error in the calculation of cost of goods sold and, therefore, an error in the calculation of gross profit and net income.

• Bias

  • Systematic, or biased, errors are errors which consistently yield results either higher or lower than the correct measurement.
  • While conducting measurements in experiments, there are generally two different types of errors: random (or chance) errors and systematic (or biased) errors.
  • If it is within the margin of error for the random errors, then it is most likely that the systematic errors are smaller than the random errors.
  • In this case, there is more random error than systematic error.
  • In this case, there is more systematic error than random error.

• Standard Error

  • In regression analysis, the term "standard error" is also used in the phrase standard error of the regression to mean the ordinary least squares estimate of the standard deviation of the underlying errors.
  • This is due to the fact that the standard error of the mean is a biased estimator of the population standard error.
  • The relative standard error (RSE) is simply the standard error divided by the mean and expressed as a percentage.
  • If one survey has a standard error of $10,000 and the other has a standard error of $5,000, then the relative standard errors are 20% and 10% respectively.
  • Paraphrase standard error, standard error of the mean, standard error correction and relative standard error.

• Chance Error and Bias

  • Chance error and bias are two different forms of error associated with sampling.
  • In statistics, a sampling error is the error caused by observing a sample instead of the whole population.
  • In sampling, there are two main types of error: systematic errors (or biases) and random errors (or chance errors).
  • Random error always exists.
  • These are often expressed in terms of its standard error:

• Computing R.M.S. Error

  • Root-mean-square error serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power.
  • RMS error is the square root of mean squared error (MSE), which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
  • RMS error is simply the square root of the resulting MSE quantity.
  • We can find the general size of these errors by taking the RMS size for them:
  • $\displaystyle \sqrt { \frac { { \left( \text{error}\ 1 \right) }^{ 2 }+{ \left(\text{error}\ 2 \right) }^{ 2 }+\cdots +{ \left( \text{error n} \right) }^{ 2 } }{ n } }$.

• Analyzing Data

  • The margin of error is a statistic used to analyze data.
  • Therefore, the absolute margin of error is 5 people, but the "percent relative" margin of error is 10% (because 5 people are ten percent of 50 people).
  • If the exact confidence intervals are used the margin of error takes into account both sampling error and non-sampling error.
  • If an approximate confidence interval is used then the margin of error may only take random sampling error into account.
  • The FPC, factored into the calculation of the margin of error, has the effect of narrowing the margin of error.

• Caveat Emptor and the Gallup Poll

  • Readers should pay close attention to a poll's margin of error.
  • The margin of error statistic expresses the amount of random sampling error in a survey's results.
  • So in this case, the absolute margin of error is 5 people, but the "percent relative" margin of error is 10% (10% of 50 people is 5 people).
  • If the exact confidence intervals are used, then the margin of error takes into account both sampling error and non-sampling error.
  • Also, if the 95% margin of error is given, one can find the 99% margin of error by increasing the reported margin of error by about 30%.

• Type I and Type II Errors

  • The two types of error are distinguished as type I error and type II error.
  • Minimizing errors of decision is not a simple issue.
  • For any given sample size the effort to reduce one type of error generally results in increasing the other type of error.
  • An example of acceptable type I error is discussed below.
  • This is an example of type I error that is acceptable.