You will need

- measuring device:
- calculator.

Instruction

1

Errors can occur as a result of various factors. Among them are the imperfection of the means or methods of measurement inaccuracies in their manufacture, failure to comply with the special conditions in conducting research.

2

There are several classifications of errors. On the submission form they can be absolute, relative and given. The former represents the difference between the calculated and the actual value. Expressed in units of the measured phenomenon and are according to the formula:∆x = hisl - Hist. The second is determined by the ratio of the absolute error to the magnitude of the true value of the indicator.The calculation formula is:δ = ∆x/Hist. Measured in percentages or fractions.

3

Reduced error of the measuring device is as the ratio of ∆x to the fiducial value XH. Depending on the type of device it is taken either equal to the limit of measurement, either related to their specific range.

4

On the conditions of occurrence distinguish between basic and advanced. If the measurements were carried out in normal conditions, there is the first kind. Deviation due to output values outside the normal, is optional. For its evaluation documentation is typically a set of rules within which may change the magnitude of a breach of the conditions of measurement.

5

The errors physical measurements are divided into systematic, random and rough. The first is caused by factors that act at multiple repetition of the measurements. The second arises from the influence of reason and random. The mistake is the result of observation, which differs sharply from all the others.

6

Depending on the nature of the measured quantity can be used various ways to measure error. The first of these is the method of Kornfeld. It is based on the calculation of the confidence interval in the range from the minimum to the maximum result. The error in this case will represent half the difference of these results: ∆x = (hmag-xmin)/2. Another way is to calculate mean square error.