An Error Analysis
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purpose of this section is to explain how and why the results deviate from the expectations. Error analysis should include a calculation of how much the results vary from expectations. This can be done by calculating the percent error observed in the experiment. Percent Error error analysis examples = 100 x (Observed- Expected)/Expected Observed = Average of experimental values observed Expected = The error propagation value that was expected based on hypothesis The error analysis should then mention sources of error that explain why your results and your expectations
Percent Error
differ. Sources of error must be specific. "Manual error" or "human error" are not acceptable sources of error as they do not specify exactly what is causing the variations. Instead, one must discuss the systematic errors in the
Error Analysis Equation
procedure (see below) to explain such sources of error in a more rigorous way. Once you have identified the sources of error, you must explain how they affected your results. Did they make your experimental values increase or decrease. Why? One can classify these source of error into one of two types: 1) systematic error, and 2) random error. Systematic Error Systematic errors result from flaws in the procedure. Consider the Battery testing experiment where the lifetime error analysis physics of a battery is determined by measuring the amount of time it takes for the battery to die. A flaw in the procedure would be testing the batteries on different electronic devices in repeated trials. Because different devices take in different amounts of electricity, the measured time it would take for a battery to die would be different in each trial, resulting in error. Because systematic errors result from flaws inherent in the procedure, they can be eliminated by recognizing such flaws and correcting them in the future. Random Error Random errors result from our limitations in making measurements necessary for our experiment. All measuring instruments are limited by how precise they are. The precision of an instrument refers to the smallest difference between two quantities that the instrument can recognize. For example, the smallest markings on a normal metric ruler are separated by 1mm. This means that the length of an object can be measured accurately only to within 1mm. The true length of the object might vary by almost as much as 1mm. As a result, it is not possible to determine with certainty the exact length of the object. Another source of random error relates to how easily the measurement can be made. Suppose you are trying to determine the pH of a solution using pH paper. The pH of the solution can be determined by
level of proficiency in speaking, writing, reading, listening) linguistic levels (i.e., pronunciation, grammar, vocabulary, style) form (e.g., omission, insertion, substitution) type (systematic errors/errors in competence vs. occasional errors/errors in
Error Analysis Chemistry
performance) cause (e.g., interference, interlanguage) norm vs. system Contents 1 Methodology 2 error analysis formula Steps in error analysis 3 See also 4 Notes Methodology[edit] Error analysis in SLA was established in the error analysis linguistics 1960s by Stephen Pit Corder and colleagues.[2] Error analysis (EA) was an alternative to contrastive analysis, an approach influenced by behaviorism through which applied linguists sought to use the formal distinctions http://sciencefair.math.iit.edu/writing/error/ between the learners' first and second languages to predict errors. Error analysis showed that contrastive analysis was unable to predict a great majority of errors, although its more valuable aspects have been incorporated into the study of language transfer. A key finding of error analysis has been that many learner errors are produced by learners making faulty inferences about the rules https://en.wikipedia.org/wiki/Error_analysis_(linguistics) of the new language. Error analysts distinguish between errors, which are systematic, and mistakes, which are not. They often seek to develop a typology of errors. Error can be classified according to basic type: omissive, additive, substitutive or related to word order. They can be classified by how apparent they are: overt errors such as "I angry" are obvious even out of context, whereas covert errors are evident only in context. Closely related to this is the classification according to domain, the breadth of context which the analyst must examine, and extent, the breadth of the utterance which must be changed in order to fix the error. Errors may also be classified according to the level of language: phonological errors, vocabulary or lexical errors, syntactic errors, and so on. They may be assessed according to the degree to which they interfere with communication: global errors make an utterance difficult to understand, while local errors do not. In the above example, "I angry" would be a local error, since the meaning is apparent. From the beginning, error analysis was beset with
How does one actually give a numerical value for the error in a measurement? The an error analysis answer to this question is in this chapter. As you will see, giving an error estimate for simple measurements is easy. The chapter consists of five sections: 2.1. Errors when Reading Scales 2.2. Errors of Digital Instruments 2.3. Standard Deviation 2.4. Histograms 2.5. Exercises << Previous Page Next Page >> Home - Credits - Feedback © Columbia University
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