Experiment Error With Equipment
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a measurement is repeated, the values obtained will differ and none of the results can be preferred over the others. Although it is not possible to do anything about such error, it can be characterized. For instance, the repeated experimental error formula measurements may cluster tightly together or they may spread widely. This pattern can experimental error examples be analysed systematically. When we measure something the measurement is meaningless without knowing the uncertainty in the measurement. This types of experimental error leads us to the idea of errors in measurement. Other factors such as the conditions under which the measurements are taken may also affect the uncertainty of the measurements. Thus when we
Sources Of Experimental Error
report a measurement we must include the maximum and minimum errors in the measurement. As an example, take measuring the height of a person, the measure may be accurate may have a scale of 1 mm. But depending on how the person being measured holds themself during the measurement we might be accurate in measuring to the nearest cm. Generally, errors can be divided into experimental error vs human error two broad and rough but useful classes: systematic and random.Systematic errors are errors which tend to shift all measurements in a systematic way so their mean value is displaced. This may be due to such things as incorrect calibration of equipment, consistently improper use of equipment or failure to properly account for some effect. In a sense, a systematic error is rather like a blunder and large systematic errors can and must be eliminated in a good experiment. But small systematic errors will always be present. For instance, no instrument can ever be calibrated perfectly. Other sources of systematic errors are external effects which can change the results of the experiment, but for which the corrections are not well known. In science, the reasons why several independent confirmations of experimental results are often required (especially using different techniques) is because different apparatus at different places may be affected by different systematic effects. Aside from making mistakes (such as thinking one is using the x10 scale, and actually using the x100 scale), the reason why experiments sometimes yield results which may be far outside the quoted errors is because of systematic effect
error'). Experimental uncertainty arises because of: Limits in the how exact the measuring apparatus is. This is the precision of the apparatus. Imperfections in experimental procedures. Judgements made by the operator. When can my results be experimental error examples chemistry said to be precise? If you repeat a measurement several times and obtain values that
Experimental Error Physics
are close together, your results are said to be precise. If the same person obtains these close values, then the experimental procedure is
Experimental Error Biology
repeatable. If a number of different people carry out the same measuring procedure and the values are close the procedure is reproducible. What is a systematic error? A systematic error is one that is repeated in each measurement http://www.splung.com/content/sid/1/page/errors taken. If this is realised after the experimental work is done, it can be taken into account in any calculations. What are random errors? Even the most careful and experienced operator cannot avoid random errors. However, their effect can be reduced by carrying out a measurement many times (if the opportunity exists) and working out an average value. Let's look in more detail at 'built-in' uncertainty of some laboratory equipment... Some measurement uncertainties are given below: EquipmentMeasurement http://www.avogadro.co.uk/miscellany/errors.htm to the nearest: Balance (1 decimal place)0.08 g Balance (2 decimal place)0.008 g Balance (3 decimal place)0.0008 g Measuring Cylinder (25 cm3)0.5 cm3 Graduated Pipette (25 cm3, Grade B)0.04 cm3 Burette (50 cm3, Grade B)0.08 cm3 Volumetric Flask (250 cm3, Grade B)0.2 cm3 Stopwatch (digital)0.01 s Calculating the percentage uncertainty (often called percentage error) ... Now try calculating the following percentage uncertainties... 1.00 g on a 2 decimal place balance 10.00 g on a 2 decimal place balance 1.00 g on a 3 decimal place balance 10 cm3 in a 25 cm3 measuring cylinder 25 cm3 in a 25 cm3 measuring cylinder 25 cm3 in a 25 cm3 graduated pipette (Grade B) 25 cm3 in a 50 cm3 burette (Grade B) 250 cm3 in a 250 cm3 volumetric flask (Grade B) 50 s on a digital stopwatch 8% 0.8% 0.08% 5% 2% 0.16% 0.32% 0.08% 0.02% Comparing uncertainties like those calculated above 'might' help you to decide which stage in an experimental procedure is likely to contribute most to the overall experimental uncertainty. How about thermometers...? Spirit filled thermometers are regularly used in college laboratories. They are often more precise than accurate. It is quite easy to read a thermometer to the nearest 0.2 °C. However, the overall calibration can be out by a degree or more. For example, for a thermometer reading 43 °C, if it is
of this type result in measured values that are consistently too high or consistently too low. Systematic errors may be of four kinds: 1. Instrumental. For example, a poorly calibrated instrument such as a thermometer that reads 102 oC when http://www.physics.nmsu.edu/research/lab110g/html/ERRORS.html immersed in boiling water and 2 oC when immersed in ice water at atmospheric pressure. Such a thermometer would result in measured values that are consistently too high. 2. Observational. For example, parallax in reading a meter scale. 3. Environmental. For example, an electrical power ìbrown outî that causes measured currents to be consistently too low. 4. Theoretical. Due to simplification of the model system or approximations in the equations describing it. For experimental error example, if your theory says that the temperature of the surrounding will not affect the readings taken when it actually does, then this factor will introduce a source of error. Random Errors Random errors are positive and negative fluctuations that cause about one-half of the measurements to be too high and one-half to be too low. Sources of random errors cannot always be identified. Possible sources of random errors are as follows: experimental error examples 1. Observational. For example, errors in judgment of an observer when reading the scale of a measuring device to the smallest division. 2. Environmental. For example, unpredictable fluctuations in line voltage, temperature, or mechanical vibrations of equipment. Random errors, unlike systematic errors, can often be quantified by statistical analysis, therefore, the effects of random errors on the quantity or physical law under investigation can often be determined. Example to distinguish between systematic and random errors is suppose that you use a stop watch to measure the time required for ten oscillations of a pendulum. One source of error will be your reaction time in starting and stopping the watch. During one measurement you may start early and stop late; on the next you may reverse these errors. These are random errors if both situations are equally likely. Repeated measurements produce a series of times that are all slightly different. They vary in random vary about an average value. If a systematic error is also included for example, your stop watch is not starting from zero, then your measurements will vary, not about the average value, but about a displaced value. Blunders A final source of error, called a blunder, is an outright mistake. A person may record a wrong value, misread a scale, forget a digi
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