Have you been asked to write a research paper on descriptive statistics or a descriptive

analysis? If so, don’t worry; we’ve come up with all the fundamental ideas you’ll need to

understand in order to write a successful paper. So, let’s get started with the foundations of

descriptive statistics. The definition of descriptive statistics will come first.

**Definition of descriptive statistics**

As per Moodle monkey, the characteristics of a group of data are summarized by a

descriptive statistic, which is a summary statistic. The method of examining such statistics all

at once is known as descriptive statistics. Descriptive statistics are not based on the

foundations of probability theory, unlike inferential statistics. Descriptive statistics are

frequently referred to be non-parametric statistics for just this reason.

**What is descriptive statistics?**

As per Moodle monkey, descriptive statistics, in summary, help in explaining and

comprehending the characteristics of a particular set of data by providing summaries of the

samples and data measurements. Since descriptive statistics only allow us to draw

conclusions based on the information provided, they are referred to as non-parametric

statistics. It is merely a straightforward method for summarising and presenting the data.

The definition and consolidation of descriptive research depend heavily on descriptive

statistics. As per Moodle monkey, it would be difficult to understand the message that the

data was reflecting because raw data is difficult to see, especially if there were numerous data

sets with various parameter values. We can more easily analyse the information gathered by

using descriptive statistics to display data in a meaningful way. For instance, it would be

challenging to quickly assess the overall performance of the students if we got 100 student’s

grades for specific or varied assignments. As per Moodle monkey experts, additionally, it

would be challenging to analyse the distribution of the marks with so many subjects

interleaved. However, we can characterize data from a single angle and calculate an average

value using descriptive statistics.

Median, mean, and mode are the most typical types of statistics that are descriptive, and they

are employed at all math and statistics levels. There are many varieties of descriptive

statistics. They will be covered more in this blog post.

**The main purpose of descriptive statistics**

As per Moodle monkey, due to the enormity of a data set, descriptive statistics are used to

interpret dense quantitative information and present it in digestible ways. GPA is an excellent

example of how descriptive statistics are applied in real-world situations. Throughout the

year, students typically take a variety of exams and classes. Numerous grades and average

percentages would result from that. GPA combines data points to give a basic view of a

student’s overall academic achievement rather than adding up all of the grades earned.

As per Moodle monkey, even when a descriptive analysis uses inferential statistics to come to

its main conclusions, descriptive statistics are still employed. Who knows how? Let’s use an

illustration to clarify. For instance, a survey of American citizens was done to see how many

people agreed that every citizen should have access to medical insurance. In this scenario, the

subject—humans—would be separated into significant subgroups with distinct demographics

and clinical traits. How can you come to a conclusion when there are so many complex

factors involved, such as average age, sex, height, weight, medical history, etc.? It would be

simpler to summarise the provided data set and develop a middle ground from all the various

sectors with the descriptive coefficients of descriptive statistics.

**Types of Descriptive statistics**

Measures of variability and measures of central tendency can both be used in descriptive

statistics. As per Moodle monkey experts, median, mean, and mode are a few examples of

measures of central tendency. Variance, Standard deviation, calculating the lowest and

maximum values of variables, as well as kurtosis and skewness, are all examples of

measurements of variability.

**There are overall four different types of descriptive statistics:**

**1. Measures of central tendency –** The centre location of a frequency distribution for data

collection is described using such metrics. These three terms—Mean, Median, and

Mode—are used to identify the points at which data are distributed.

**2. Measures of frequency –** This displays how frequently something has happened and offers

three types: count, percentage, and frequency. This can be used to demonstrate how

frequently a subject has responded.

**3. Measures of position –** As per Moodle monkey experts, if the responses are standardized,

this type of descriptive statistic, which includes percentile ranks and quartile ranks,

emphasizes how one response is related to another.

**4. Measures of variation and dispersion-** The range, variance, and standard deviation are

the three main components of this type. By stating the intervals, it identifies the data

distribution. The high and low points are shown by the range. The difference between the

observed score and mean can be seen using variance and standard deviation. This kind is used

to demonstrate how evenly distributed the data is.