Error: Estimation | Amplification | Propagation

The estimation of errors depends on two essential factors: (i) the definition
of accuracy and (ii) available information about an error (noise). For example,
the statement that the quantity * A* has the value * a* ± δ*a*
implies that (i) the measure of accuracy is the whole span of values, 2 |δ*a*|,
and (ii) the error takes any value from the interval [-δ*a*, δ*a*].
Note, that this does not specify the distribution of errors within that
interval. However, it does imply that the value * a* does not have a
systematic error (is not biased). This itself is a very strong statement, which requires the
proof of its validity.

Suppose we have two quantities *A* and *B* with the values *a* ± δ*a*
and *b* ± δ*b*, respectively. According to the above
definition of accuracy and the assumption about the errors, ranges of
the values of A and B are [* a* - |δ*a*|, * a* + |δ*a*|]
and [*b* - |δ*b*|, *b* + |δ*b*|]. The range of
values of their sum is

[a + b - |δa| - |δb|, a
+ b + | δa| + | δb|] |
(1) |

In other words, the the quantity *C* = *A* + *B* has the value
*c* = *a* + *b* and the error |δ*c*| = | δ*a*|
+ |δ*b*|. From this we can formulate that the absolute errors are always
added.

Eq. (1) can be generalized for a weighted sum

(2) |

as follows

(3) |

A relative error for the quantity C is defined as η*c* = | δ*c*| /*c*.
For the case of a sum of two quantities

ηc = ( | δa| + | δb| )/(a
+ b) |
(4) |

The relative error can be very large if * a* and * b* are of opposite sign and almost cancel each
other.

If large errors are relatively rare then some typical errors can be used as a
measure of accuracy. A popular choice is the standard deviation. However, the
calculation of the standard deviation requires detailed information about the
distribution of errors. This means that we have to know the distribution function *p*(*a*)
(or make some reasonable assumption about *p*).

The definitions of the standard deviation σ and variance var are

(5) |

where

(6) |

is the mean value (often denoted as μ or ).

This is the case when all errors within the interval [-δ*a*,
δ*a*] are equally probable:

Fig. 1. The uniform (rectangular) distribution. |

The standard deviation is

(7) |

For the uniform distribution σ ~ 0.6 δ*a*. The standard
deviation does not take into account about 40% of equally probable errors. It
means that σ is not quite suitable as a measure of accuracy for this particular
error distribution. However
= 1.7320508075688772 σ accounts for 100% of errors.

The normal distribution, also called Gaussian distribution, is defined as

(8) |

Here σ is the standard deviation and μ is the mean value.

This distribution extends to ± infinity and δ*x* cannot be
used as a measure of accuracy. The interval ± σ accounts for
68% of all errors. More values are listed below:

L /σ |
P(μ - L < x < μ + L) |

1 | 0.6826894921370859 |

2 | 0.9544997361036416 |

3 | 0.9973002039367398 |

4 | 0.9999366575163338 |

5 | 0.9999994266968563 |

6 | 0.9999999980268247 |

From the above table we can tell that 95.4% of all values are in the interval μ ± 2σ. This interval is called the confidence interval and 95.4% is the confidence level. The values μ - 2σ and μ + 2σ are also called the confidence limits.

Let as present each variable in (2) as its mean value and an error:

A = _{ n}a + ε_{ n}. _{ n} |
(9) |

According to the definition, the variance is

(10) |

It can be rewritten as

(11) |

Under the assumption that the errors for different variables are ** independent**
(uncorrelated) and the covariance can be set to zero in (11). From this we can formulate that the
variances of uncorrelated variables are additive.

* Remark.* The distribution of a sum is not necessarily of the
same type as for the individual components. For example, the sum of two variables
with the uniform distribution (see Fig. 1) has the trapezoidal distribution:

The arithmetic mean of *N* measurements is defined as

(12) |

It is also called the population average. Eq. (12) can be considered as a definition of the new random variable with its mean value and variance. The mean value of (12) is the same as for an individual measurement. The variance depends on the way the measurements were made.

If the measurements are ** independent** (uncorrelated) then from (11) we have

(13) |

and the standard deviation is

(14) |

We can conclude that the repetition of experiments and averaging **reduce the error**.

Sometimes one can see (*N* - 1) instead of *N* in
denominators of the equations similar to (13-14). There is no contradiction. The above accuracy estimation
is based on independent information about the mean values of *A *(e.g. from
a distribution function). In practice, the mean value itself is often estimated as a
population average *A _{ mean}* (12). Then the random variables Δ

(15) |

and

(16) |

The correct estimation of the variance is

(17) |

For practical variance estimation the following formula is used

(18) |

The difference between Eqs (13) and (18) reflects the difference in the available
information. Eq. (13) is the variance relative to the **known** mean value.
Eq. (18) is the best estimate of the variance along with the estimate of the
mean (12). This illustrates the fact that the estimation of errors depends on the
information about the random variables.

Error: Estimation | Amplification | Propagation

© Nikolai Shokhirev, 2001 - 2017

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