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Statistics is mainly classified as either descriptive or inferential. Descriptive statistics refers to quantitatively analyzing and describing the main features of a population regarding based on a selected sample. The aim is to summarize the sample that is studied in order to understand the population that the sample is representing. Descriptive statistics provide sample summaries. Simple tabulations are used to highlight descriptive data. In this type of statistics, no conclusions can be made beyond the analyzed group. Descriptive statistics can be used to study a spread of marks by using such parameters as the mean, mode and the median. On the other hand, inferential statistics is used to study big populations. Such is done by using samples to generalize about a whole population from which the samples are drawn. The sample should be accurate in order to meet the requirement of representativeness.

In any study, one of the two or both types of statistics must be used. However, the nature of a study dictates the choices that are made. Where a huge population is to be studied, then it is practical to embrace inferential statistics by sampling and then collecting data regarding the samples, which should be representative of the whole population. Where a small group is to be analyzed, descriptive statistics should be used.

The problem with inferential statistics is that an assumption that a sample is representative of the population is made. The assumption may turn out to be false. Therefore, the inferences made from studies employing the approach could be misleading. With descriptive statistics, only a particular group is studied. Hence, no inference is made outside the group. Thereby, it is relatively less appropriate to analyze a big population using the approach. In studying education and inclusion, levels of education and inclusion will be major indices. Irrespective of the strengths and weaknesses of the two approaches to statistics, they remain useful. Since I intend to conduct an analytical study, I will employ both approaches.

Code: Sample20

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