Descriptive statistics give the simplest meaningful way of illustrating data. Essentially, they summarize data into something evocative. In essence, descriptive statistics can convey data in recognized patterns like charts and graphs, among others. Descriptive statistics have various benefits for data analysts. Firstly, they summarize large data into small presentations that can be visualized easily. Descriptive statistics provide the quantitative features of data that gives it a meaning. For instance, in presenting marks for 200 students, descriptive statistics would enable the analyst to evaluate the overall performance of the students. In addition, descriptive statistics would enable the analyst to evaluate the spread of the marks. Descriptive statistics can utilize various ways of expressing data in a meaningful manner. These ways include measures of central tendency, measures of spread, and tabulated description. Moreover, descriptive statistics allow for the utilization of both numerical and graphical approaches to ensure precise and objective illustrations (Boeree, 2005).
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Differences between Descriptive and Inferential Statistics
While descriptive statistics summarize data in a meaningful way, inferential statistics go beyond a summary of data. In fact, inferential statistics are used to gain insight into the data analyzed. Deductions are usually drawn from data when utilizing inferential statistics. While inferential statistics utilize samples from data, descriptive statistics tend to use the whole data (Lowry, 2015). For instance, inferential statistics would use samples from a population to deduce their behaviors. Descriptive statistics utilize different methods to analyze data; this is unlike inferential statistics.
While descriptive statistics utilize measures of central tendency and measures of spread, inferential statistics utilize hypothesis testing and estimation. In essence, inferential statistics utilize samples to generalize populations from which samples are taken. On the other hand, descriptive statistics utilize parameters to make data meaningful and useful to analysts. It is also worth noting that inferential statistics are usually not the perfect description of the population because they utilize samples. In contrast, descriptive statistics tend to give a precise illustration of data (Lane, 2013).
Summary of Measures of Central Tendency, Standard Deviation, and Their Relation
Central tendency is a term utilized to emphasize the fact that a number can best describe an entire set of measurements. In fact, the number is considered central to the set. Measures of central tendency constitute the mode, the mean, and the median. While the mode refers to the measurement with the highest frequency, the median refers to the number that can be considered as the centrality of the set. On the other hand, the mean refers to the average of the measurements (Hiray, 2008).
Measures of spread can also be done with respect to a given data. This requires the use of dispersion. Dispersion emphasizes the fact that there is a number that describes how the measurement spread out from the first number. In essence, dispersion illustrates the standard deviation. It refers to the extent to which scores depart from the mean. It is an estimation of the expected value that shows how far the scores are from the mean (Hiray, 2008).
Essentially, it can be observed that standard deviation measures the extent of deviation from the mean. The main aim of the central tendency is usually to find a single number which is representative of the whole set of measurements. In contrast, the aim of the standard deviation is to provide the degree of spread from the mean. Standard deviation complements the central tendency in statistics (Hiray, 2008).
Boeree, G. (2005). Descriptive statistics. Web.
Hiray, J. (2008). Measures of central tendency and variability. Web.
Lane, D. (2013). Inferential statistics. Web.
Lowry, R. (2015). Concepts and application of inferential statistics. Web.