Reviewed by Oct 05, 2020| Updated on
Descriptive statistics are brief descriptive coefficients summarising a given collection of data, which can either be a representation of the total population or its subset. Descriptive statistics are split into central trend measurements and variability measurements (spread).
Measures of central tendency include mean, median, and mode. In contrast, variable measures include standard deviation, variance, the maximum and minimum variables, the skewness and kurtosis.
In short, descriptive statistics explain and clarify the characteristics of a specific data set by giving summaries of the data sample and steps. Measures of the centre are the most known forms of descriptive statistics, such as the mean, median, and mode, used at every level of math and statistics.
The mean, or average, is determined by adding all the figures in the data set and then dividing them by the count of numbers in the sample. The sum of the following dataset, for instance, is 45: (7, 8, 9, 10, 11). The mean will be 9 (45/5).
A mode is a most frequently displayed value in a data set, and the median is the figure located in the centre of the data set. It is the figure which separates the higher numbers from the lower numbers within a collection of data. Less common forms of descriptive statistics, however, are still very relevant.
People use descriptive statistics to repurpose quantitative observations that are hard to grasp into bite-sized explanations through a broad data set.
For example, the grade point average (GPA) of a student gives a clear interpretation of the descriptive statistics. The concept of a GPA is to take data points from a wide variety of tests, classes, and grades and average them together.
It provides a general understanding of the overall academic abilities of a student. Personal GPA for a student reflects its mean academic results.
All descriptive statistics are either central trend measurements or variability measurements, also known as dispersion tests. Central tendency measures focus on the average or middle values of data sets, while variability measures concentrate on data dispersal. Such two initiatives use maps, tables, and general forums to help people understand the nature of the data being analysed.
Central tendency indicators define a distribution's core location for a data set. An individual analyses and establishes the frequency of each data point in the distribution using the mean, median, or mode that measures the most common patterns of the data set being analysed.
Variability tests or spread measurements determine whether the distribution for a collection of data is spread out. Variability indicators help to explain this by defining the data set's shape and distribution. Scale, quartiles, absolute deviations, and variances are all examples of variability measurements.