Cause Of Error In Experiment
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the measurement devices (hard to read scales, etc.) - Usually caused by poorly or miscalibrated instruments. - There are usually ways to determine or estimate. - Cannot reduce by repeated measurements, but percent error experiment can account for in some way. 3. Indeterminate (Random) Errors
- Natural variationsSources Of Error In Experiments
in measurements. - May be result of operator bias, variation in experimental conditions, or other factors not easily accounted for. types of error in experiments - May be minimized by repeated measurement and using an average value. Experimental results may be described in terms of precision and accuracy. Precision - relatively low indeterminate error.
- reproducibility. human error in experiments - high precision means a number of readings or trials result in values close to the same number. Accuracy - relatively low determinate error. - close to a true value. Accurate and precise Precise but not accurate Reliability- a procedure is said to be reliable if it may be completed with a high degree of accuracy and precision. For most of our investigations we will beExamples Of Sources Of Error In Experiments
concerned with the precision of results. Experimental Data and Measures of Uncertainty
Quantities that give some measure of experimental precision are Deviation (individual values) Average deviation Average Deviation of the Mean (Standard Average Deviation) Sample standard deviation (sometimes denoted as ) Standard error It is customary to report experimental results with an uncertainty in the following form Result = Average ± uncertainty The uncertainty is one of the measures of precision given above (a.d., A.D., s, or Sx). For our present cases we will use standard error and report results as Result = Average ± Sx This information is simply preliminary to analyses we will be performing on some sample data, and data we will collect in the future. The idea here is to give you the formulae that are used to describe the precision of a set of data. We will see a bit more later. We need to see a calculation of these quantities. These pages illustrate one run through of calculations Another document will be about what these statistical quantities might tell us and how we might use this information to make certain decisions (usually as concerns elimination of data.) Reading Instruments and Errors Recorded values should rWe're using the word "wrong" to emphasize a point. All experimental data is imperfect. Scientists know that their results always contain errors. However, one of their goals is to causes of experimental error minimize errors, and to be aware of what the errors may be. Significant causes of experimental error in chemistry digits is one way of keeping track of how much error there is in a measurement. Since they know that
What Can Cause Experimental Error
all results contain errors, scientists almost never give definite answers. They are far more likely to say: "it is likely that ..." or "it is probable that ..." than to give an exact http://www.ahsd.org/science/stroyan/hphys/stats/meas_uncert_1.htm answer. As a science student you too must be careful to learn how good your results are, and to report them in a way that indicates your confidence in your answers. There are two kinds of experimental errors. Random Errors These errors are unpredictable. They are chance variations in the measurements over which you as experimenter have little or no control. There is just as great a http://www.digipac.ca/chemical/sigfigs/experimental_errors.htm chance that the measurement is too big as that it is too small. Since the errors are equally likely to be high as low, averaging a sufficiently large number of results will, in principle, reduce their effect. Systematic Errors These are errors caused by the way in which the experiment was conducted. In other words, they are caused by the design of the system. Systematic errors can not be eliminated by averaging In principle, they can always be eliminated by changing the way in which the experiment was done. In actual fact though, you may not even know that the error exists. Which of the following are characteristics of random errors? Check all that apply. a) doing several trials and finding the average will minimize them b) the observed results will usually be consistently too high, or too low c) proper design of the experiment can eliminate them d) there is no way to know what they are It is not easy to discuss the idea of systematic and random errors without referring to the procedure of an experiment. Here is a procedure for a simple experiment to measure the density of rubbing
mistake. Examples would be when you set up your experiment incorrectly, when you misread an instrument, or when you make a mistake in a calculation. Human errors are not a http://www2.volstate.edu/Phy/PHYS2110-2120/experimental_error.htm source of experimental error; rather, they are “experimenter's” error. Do not quote human error as a source of experimental error. Systematic error is an error inherent in the experimental set up which causes the results to be skewed in the same direction every time, i.e., always too large or always too small. One example of systematic error would be trying to measure the fall time of a error in ping pong ball to determine the acceleration due to gravity. Air resistance would systematically reduce the measured acceleration, producing a systematic error. Some systematic errors can be easily corrected. For example, if a balance reads 0.25 g when there is no mass on it, this would introduce a systematic error to each mass measurement—they would all be too large by 0.25 g. This can be corrected by error in experiment zeroing the balance. Other systematic errors can only be eliminated by using a different experimental setup. Most of the simple experiments you do will have some systematic error. All experiments have random error, which occurs because no measurement can be made with infinite precision. Random errors will cause a series of measurements to be sometimes too large and sometimes too small. An example of random error could be when making timings with a stopwatch. Sometimes you may stop the watch too soon, sometimes too late. Either case introduces random error in your measurements. (Note that when a human is involved in the actual measurement process, he/she can introduce valid experimental error that is not within the definition of human error. Your finite reaction time is not a mistake; it is a limitation of one part of the experimental process, the human making the measurement.) Random error can be reduced by averaging several measurements. ERROR ANALYSIS One way to analyze experimental error is with a % error calculation. The % error is useful when you have a single experimental result that you wish to compare with a standard value, or when you have two experimental values obtained by