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Masses of unorganized data — such as the census of population, the weekly earnings of thousands of computer programmers, etc. — are of little value as is. However, descriptive statistics can be used to organize data into a meaningful form. Descriptive statistics refers to methods of organizing, summarizing, and presenting data in an informative way.

Once we have identified our population and collected the sample data, our goal is to describe the characteristics of the sample in an accurate and unambiguous fashion in such a way that the information will be easily communicated to others. Describing, or just summarizing, the data can be done in two ways – either graphically or numerically. We now list some of the advantages and disadvantages of descriptive statistics.

1. Descriptive statistics allows us to present the data in graphical formal. Data presented in a visual form is much easier to understand. Qualitative data can be presented in the form of bar charts and pie charts. Numerical data can be presented in the form of dot plots and histograms.
2. The various statistical measures allow us to summarize the central characteristics of the data. For example, the mean measures the central tendency of the data values. This allows us to obtain a rough understanding of where the data values lie. This is very important when we are dealing with a large amount of numerical data.
3. The measures of dispersion such as standard deviation help us to understand how far the data values are spread away from each other. This is important because it is not easy to determine how spread apart the data values are when dealing with huge data sets.
4. We can understand the shape of the distribution by computing the measures of skewness and kurtosis.
5. Correlation analysis allows us to compare two different characteristics and check whether there is any relation between them. For example, we can check whether there is any correlation between height and weight by computing the correlation coefficient of the heights and weights of a sample of 100 individuals.

1. We cannot use descriptive statistics to make any kind of predictions on the basis of the given data values. The tools of inferential statistics such as regression analysis allow us to make predictions about future values of the variable.
2. The data collection process is generally time-consuming and expensive. For example, conducting a survey in order to collect data involves a lot of work. It is a time-consuming process and quite expensive since we need to train and pay the interviewers who conduct the survey.
3. Descriptive statistics can be misused in order to deceive and give a false impression to the general public. For example, simply changing the scale of a graph can lead to misleading conclusions by a layman not trained in statistics.