Accuracy and Precision. Importance of Scientific Measurements in Essay

Accuracy and Precision. Importance of Scientific Measurements in Ranges - Essay Example Lastly; it can as well be defined as the degree of the proximity of the values under measurement. Precision on the other hand shows how close the two or more values under measurements are. The two terms are different in that one may be accurate but imprecise on the other hand one can as well be precise but not accurate. Measurements versus Accuracy The fact that accuracy and precision are two independent entities makes a value termed as precise to be either true or false depending on the accuracy of the measurement scale. Therefore it is false to that a measurement of high precision must exhibit high accuracy. Statistical Error and Systematic Error Statics error is that which arises as a result of biasness that is caused by the random fluctuations in statics which can be controlled or reduced by accuracy. On the other hand, systematic error is the error that results from the introduction of the biasness that results to systematic which is difficult to be eliminated. Two errors differ in the following way; first, while systematic errors arise because of the random fluctuations the statistical results from the experienced biasness. Systematic error is due to the introduced bias while the statistical is due to the random fluctuation. The elimination of systematic error is impossible while the latter is possible. Lastly, systematic error arises from the system while the statistics error is from the statics.

Dq -5-Terence Essay Example | Topics and Well Written Essays - 1500 words

Dq -5-Terence - Essay Example The discussion below elaborates and helps in understanding various aspects of research. Research questions that address a problem are concerned with quantitative research. In this case, the questions give an exact description of a phenomenon. Since it has to answer a question, control is exercised in order to eliminate any form of bias that may arise during the research. In most cases, the information under this kind of research relies on cross-functional approaches and the data reduced to numerical codes for easy analysis. On the contrary, questions that might be used in an interview heavily rely on qualitative research. This indicates that the information under such a research does not require discrete numerical data but only seeks for explanations about a phenomenon. However, since researchers determine what is asked, there is a likelihood of biasness because they decide how to fit the questions in a situation. These questions employ verbal, and in other cases, use pictorial descriptions for effective understanding and analysis. According to Palinkas et al. (2013), both qualitative and quantitative research methods have a trace of bias. It is extremely difficult to eliminate bias in research. Using the methods concurrently helps researchers to cub bias since both methods can be used in checking each other. They actually complement each other. By In terms of data preparation, both methods, in a unique way require the use of verbal descriptions. They are a great pillar in understanding in research. Researchers are increasingly finding it difficult using the methods independently because they are intertwined together and just separated by a thin line. An effective combination provides quality research findings that address the problem and offer solutions in a logical and acceptable manner. Questions that address a problem are objective in nature because they have