Identify Any Sources Of Error In Measurements
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Types Of Sources Of Error
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Sources Of Error In Experiments
Brazil Canada France Germany India Indonesia Italy Malaysia Mexico New Zealand Philippines Quebec Singapore Taiwan Hong Kong Spain Thailand UK & Ireland Vietnam Espanol About About Answers Community Guidelines Leaderboard sources of error in a chemistry lab Knowledge Partners Points & Levels Blog Safety Tips Education & Reference Homework Help Next What are possible sources of error in an experiment? My experiment is on testing nutrients in solutions, using test tubes and hot water baths, i need two sources of error, thanks:) 3 following 5 answers 5 Report Abuse Are you sure you want to delete sources of error in physics this answer? Yes No Sorry, something has gone wrong. Trending Now Tyler Hoechlin Hilary Duff Barack Obama Cheryl Burke Kris Kristofferson iPhone 7 Plus Dolly Parton Free Credit Report Kristen Bell Luxury SUV Answers Relevance Rating Newest Oldest Best Answer: Incomplete definition (may be systematic or random) - One reason that it is impossible to make exact measurements is that the measurement is not always clearly defined. For example, if two different people measure the length of the same rope, they would probably get different results because each person may stretch the rope with a different tension. The best way to minimize definition errors is to carefully consider and specify the conditions that could affect the measurement. Failure to account for a factor (usually systematic) - The most challenging part of designing an experiment is trying to control or account for all possible factors except the one independent variable that is being analyzed. For instance, you may inadvertently ignore air resistance when measuring free-fall acceleration, or you may fail to account for the effect of
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 can account for in some
Source Of Error Definition
way. 3. Indeterminate (Random) Errors
- Natural variations in measurements. - May be different types of errors in measurement result of operator bias, variation in experimental conditions, or other factors not easily accounted for. - May be minimized by repeatedExamples Of Experimental Errors
measurement and using an average value. Experimental results may be described in terms of precision and accuracy. Precision - relatively low indeterminate error.
- reproducibility. - high precision means a number of readings or https://answers.yahoo.com/question/index?qid=20090707145338AAaUiOa 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 be concerned with the precision of results. Experimental Data and Measures of Uncertainty Quantities http://www.ahsd.org/science/stroyan/hphys/stats/meas_uncert_1.htm 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 reflect the precision of an instrument. Recorded values should have at least one more place than the smallest division on the scale of the instrument. Readings from a meter stiassumes that any observation is composed of the true value plus some random error value. But is that reasonable? What if all error is not random? Isn't it possible that some errors are systematic, that they hold across most or all of the http://www.socialresearchmethods.net/kb/measerr.php members of a group? One way to deal with this notion is to revise the simple true score model by dividing the error component into two subcomponents, random error and systematic error. here, we'll look at the differences between these two types of errors and try to diagnose their effects on our research. What is Random Error? Random error is caused by any factors that randomly affect measurement of the variable across the sample. For instance, each person's of error mood can inflate or deflate their performance on any occasion. In a particular testing, some children may be feeling in a good mood and others may be depressed. If mood affects their performance on the measure, it may artificially inflate the observed scores for some children and artificially deflate them for others. The important thing about random error is that it does not have any consistent effects across the entire sample. Instead, it pushes observed scores up or down sources of error randomly. This means that if we could see all of the random errors in a distribution they would have to sum to 0 -- there would be as many negative errors as positive ones. The important property of random error is that it adds variability to the data but does not affect average performance for the group. Because of this, random error is sometimes considered noise. What is Systematic Error? Systematic error is caused by any factors that systematically affect measurement of the variable across the sample. For instance, if there is loud traffic going by just outside of a classroom where students are taking a test, this noise is liable to affect all of the children's scores -- in this case, systematically lowering them. Unlike random error, systematic errors tend to be consistently either positive or negative -- because of this, systematic error is sometimes considered to be bias in measurement. Reducing Measurement Error So, how can we reduce measurement errors, random or systematic? One thing you can do is to pilot test your instruments, getting feedback from your respondents regarding how easy or hard the measure was and information about how the testing environment affected their performance. Second, if you are gathering measures using people to collect the data (as interviewers or observers) you should make sure you train them thoroughly so that they aren't inadvertently introducing error. Third, when yo