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Descriptive statistics is like a summary of any statistical information or data which is either representation of an entire population or a sample of that population. The descriptive statistics are broken into measures of central tendency (mean, median, mode) and measures of variability (standard deviation, variance, min or max variables, skewness and kurtosis).
Coming to the crux, descriptive statistics helps in understanding and describing the features of a specific set of data by giving a precise summary of the measures of the sample data. Mean, mode and median are the most recognized one and are used at almost all levels of mathematics in statistics.
People use it to repurpose incomprehensible quantitative insights of a huge data set into bitesized descriptions. The grade point average is a great example to understand descriptive statistics. The idea behind this GPA concept is that the data points of a student are collected from a wide variety like classes, exams and other activities like viva projects and then average them to get a general overview of the overall performance of the student. One’s personal GPA shows his/her academic performance.
The descriptive statistics are measured in two forms namely central tendency and variability.
Both of these measures use graphs, tables and general discussions which will help people in a better understanding of the data analysis. The measures of the central tendency tell the central position of a data set. Frequency of each data in the distribution is analyzed using mean, mode and median which are the most common ways to analyze these data sets.Whereas measures of variability also known as the measure of spread focus on how to spread out the distribution. The data distribution is not described in this (like we know that in central tendency we have the central position or the average of it).
Advantages of descriptive statistics
 The environment of observation is natural or unchanged
 It may be used as a precursor to future researches
 Data collection allows indepth gathering of data for both quantitative and qualitative
 A large amount of quality data is collected
Disadvantages of descriptive statistics
 The participants or the subjects might not behave truthfully or naturally while they are being observed.
 Can’t be used to correlate variables or determine the cause and its effects
 The result of the research may be open to interpretations
 The study cannot be replicated

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