precision - the degree of consistency and agreement among independent measurements of a quantity under the same conditions [Fluke, G-11]. So, we can state the diameter of the copper wire as 0.72 ± 0.03 mm (a 4% error). Another way of saying the same thing is that the observed spread of values in this example is not accounted for by the reading error. Fluke Corporation: Everett, WA, 1994.

It also varies with the height above the surface, and gravity meters capable of measuring the variation from the floor to a tabletop are readily available. In[20]:= Out[20]= In[21]:= Out[21]= In[22]:= In[24]:= Out[24]= 3.3.1.1 Another Approach to Error Propagation: The Data and Datum Constructs EDA provides another mechanism for error propagation. In[39]:= In[40]:= Out[40]= This makes PlusMinus different than Datum. Rule 2: Addition and Subtraction If z = x + y or z = x - y then z Quadrature[x, y] In words, the error in z is the quadrature of

The object of a good experiment is to minimize both the errors of precision and the errors of accuracy. Top ACCURACY, RELIABILITY AND VALIDITY These three terms are often used when referring to experiments, experimental results and data sources in Science. You get a friend to try it and she gets the same result. Calibration: Philosophy and Practice, 2nd.

The transcendental functions, which can accept Data or Datum arguments, are given by DataFunctions. Pugh and G.H. An ammeter for instance may show a reading of 0.2A when no current is flowing. The correct value of the measurand [Fluke, G-15].

M L2T-2. Random error – this occurs in any measurement as a result of variations in the measurement technique (eg parallax error, limit of reading, etc). This means that the diameter lies between 0.704 mm and 0.736 mm. In[43]:= Out[43]= The above number implies that there is meaning in the one-hundred-millionth part of a centimeter.

We close with two points: 1. V = IR Imagine that we are trying to determine an unknown resistance using this law and are using the Philips meter to measure the voltage. When making a measurement, read the instrument to its smallest scale division. The two different types of error that can occur in a measured value are: Systematic error – this occurs to the same extent in each one of a series of measurements

Similarly for many experiments in the biological and life sciences, the experimenter worries most about increasing the precision of his/her measurements. Thus, the specification of g given above is useful only as a possible exercise for a student. The standard error of the estimate m is s/sqrt(n), where n is the number of measurements. For example, errors in judgment of an observer when reading the scale of a measuring device to the smallest division. 2.

In[11]:= The number of measurements is the length of the list. Such variations are normal. We find the sum of the measurements. The Gaussian normal distribution.

In such cases statistical methods may be used to analyze the data. Nonetheless, you may be justified in throwing it out. Due to simplification of the model system or approximations in the equations describing it. A simple example is zero error, where the instrument has not been correctly set to zero before commencing the measuring procedure.

In[6]:= Out[6]= We can guess, then, that for a Philips measurement of 6.50 V the appropriate correction factor is 0.11 ± 0.04 V, where the estimated error is a guess based We may obtain a set of readings in mm such as: 0.73, 0.71, 0.75, 0.71, 0.70, 0.72, 0.74, 0.73, 0.71 and 0.73. Other scientists attempt to deal with this topic by using quasi-objective rules such as Chauvenet's Criterion. Write one non-zero figure before the decimal point and correct the magnitude of the number by using the appropriate power of ten.

The only problem was that Gauss wasn't able to repeat his measurements exactly either! So, as you use the instrument to measure various currents each of your measurements will be in error by 0.2A. How about if you went out on the street and started bringing strangers in to repeat the measurement, each and every one of whom got m = 26.10 ± 0.01 g. For example, if the error in a particular quantity is characterized by the standard deviation, we only expect 68% of the measurements from a normally distributed population to be within one

If you wish, you could quote the error estimate as two standard deviations. For further information read: http://www.nature.com/news/kilogram-conflict-resolved-at-last-1.18550 . 2.The metre is defined as the length of the path travelled by light in a vacuum during a time interval of 1/299 792 458 Clearly, you need to make the experimental results highly reproducible. Accurate measurements do not ensure an experiment is valid or reliable.

m = mean of measurements. The two terms mean the same thing but you will hear & read both in relation to science experiments & experimental results. By default, TimesWithError and the other *WithError functions use the AdjustSignificantFigures function. LT-1; b.

For a series of measurements (case 1), when one of the data points is out of line the natural tendency is to throw it out. Clearly this experiment would not be valid or reliable (unless it was carried out in vacuum). We have already seen that stating the absolute and relative errors in our measurements allows people to decide the degree to which our experimental results are reliable. mistake or blunder - a procedural error that should be avoided by careful attention [Taylor, 3].

discrepancy - a significant difference between two measured values of the same quantity [Taylor, 17; Bevington, 5]. (Neither of these references clearly defines what is meant by a "significant difference," but Winslow, The Analysis of Physical Measurements (Addison-Wesley, 1966) J.R.