Importance of Scaling and How Sampling Method Measures Accuracy (Essay Sample)

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Scaling is a technique that heavily relies upon measurement accuracy. In order for a statistician to generate the right scale for certain objects, it is recommended to use the reliable measurement technique that will provide accurate numbers CITATION Ali17 \l 1033 (Yousefi & Ng, 2017). There are different measurement techniques that can be applied during the process of measuring certain variables. However, choosing the right measurement is important as it enables accuracy and prescient during the process of scaling. This is achieved by carrying out all the operations and task that may include addition, subtraction, multiplication, and division in order to come out with the right outcomes.
In addition, choosing the right measure during the process of measurement play an important role in developing accurate values. Therefore it is necessary to ensure that the right measuring technique is applied depending on the type of substance to be measured. When measuring certain objects, it is quite different from measuring liquids given the disparities in their states of matter CITATION Pey16 \l 1033 (Poozesh, Sabino, Baqersad, Avitabile, & Niezrecki, 2016). It is, therefore, a requirement for the statistician to develop the measurement technique for accurate results.
Accurate results facilitate easy development of a scale in regard to the objects being scaled. This is because they represent the true picture of the variables being measured. Accurate results will also enable the person developing the scale regardless of the scaling technique being used. Because after using the most reliable form of measurement the desired outcomes are achieved during the process of scaling the variables. Therefore, whether an individual uses comparative scaling techniques that involves comparing two objects at the same time or using non-comparative scaling techniques.
Sampling techniques are important in collecting data from large sets of data. They enable us to develop estimates of required from data understudy. In most cases, they do not provide us with the actual values but just an approximate value on the statistical data. This results from both the sampling errors and non-samplings errors that cumulatively have a general impact on the outcome results. Sampling errors are usually caused when the statistician just collects data from a portion of a given group of data, rather than collecting the required sample of data CITATION Chr15 \l 1033 (Wolf, Degner, & Briz, 2015). Therefore, this causes disparities from the expected values.
The non-sampling errors are usually certain deviations that arise from the actual data values. They may include coverage errors that are usually related with failure to precisely represent all information regarding a particular sample of data. Such errors in the process of sampling, therefore, create ambiguous results that are quite different from the expected results CITATION Cao18 \l 1033 (Cao, et al., 2018). Therefore, regardless of the sampling technique a person may use in the collection of data, this does not have a large impact on the outcome of the result. But, the intensity of non-sampling errors. This is because they cause enormous errors in the process of sampling data from a given population CITATION Jer16 \l 1033 (Stapleton, Hillhouse, Coups, & Pagoto, 2016). In addition, the respondents during the process of data collection may fail to cooperate during this process and as a result, they could be hiding some information from the researcher for some reasons. This could be because of some deliberate reasons or maybe they feel it is not right to share such kind of information. Some of them could even provide the wrong piece of information.
Consequently, this has a large impact on the expected outcome. Hence, the magnitude of errors that the non –sampling errors have on the overall result is relatively bigger than that caused by sampling errors. This, therefore, implies that regardless of the sampling technique being used in the process of data collection. Lastly, errors arising from mistakes during data collection also plays a big role in altering the general result as it provides wrong values during the process of sampling.
Data circumvention during the process of presenting large sets of data in a project has proven to be the most reliable way of dealing away with the cumbersome and time-consuming activities in this process. It enables the researcher to save on time especially when dealing with time-limited projects that involve the analysis of large data. Therefore it is necessary to learn on how to circumvent data during this process so as to ensure that all the