*Seven Myths in Error Analysis.*

**Myth 1.** *Random
errors can always be determined by repeating measurements under identical
conditions.*

Although it
is shown in one case (Problem 6.3.) that the inductive and the deductive method
provide practically the same random errors, this statement is true only for time-related
random errors (Sect. 6.2.5).

**Myth 2.** *Systematic
errors can be determined from the fluctuation of the data (i.e., inductively).*

It should
be quite obvious that it is not possible to determine the scale error from the
pattern of data values (Sect. 7.2.4).

**Myth 3.** *Measuring
is the cause of all errors.*

The
standard example of random errors, measuring the count rate of radiation from a
radioactive source repeatedly, is not based on measurement errors but on the
intrinsic properties of radioactive sources (Sect. 6.2.1). Usually, the
measurement contribution to this error is negligible.

Just as
radiation hazard is most feared of all hazards because it is best understood,
measurements are thought to be the intrinsic cause of errors because their
errors are best understood.

**Myth 4.** *Counting
can be done without error.*

Usually,
the counted number is an integer and therefore without (rounding) error.
However, the best estimate of a scientifically relevant value obtained by
counting will always have an error. These errors can be very small in cases of
consecutive counting, in particular of regular events, e.g., when measuring
frequencies by counting (Sect. 2.1.4).

**Myth 5.** *Accuracy
is more important than precision.*

For single
best estimates, be it a mean value or a single data value, this question does
not arise because in that case there is no difference between accuracy and
precision. (Just like shooting only once at a target, Sect.
7.6.) Generally, it is good practice to balance precision and accuracy.
The actual requirements will differ from case to case.

**Myth 6.** *It
is possible to determine the sign of an error.*

It is
possible to find the signed deviation of an individual data value but the sign
of the error of a best estimate, be it systematic or random, cannot be
determined because the true value cannot be known (Sect. 7.2.1). The use of the
term *systematic error* for a
systematic *deviation* is misleading
because a deviation is not an uncertainty at all.

**Myth 7.** *It
is all right to “guess” an error.*

The uncertainty
(the error) is one of the characteristics of a best estimate, just like its
value, and nearly as important. Correct error analysis saves measuring time and
total cost. A factual example for that is given in Sect. 10.1.1 where correct
error analysis could have saved 90% of the cost.