What is Statistics?

The availability of data is a massive boon in today’s world. The greatest challenge is, however, to analyze it for our needs. It is essential to understand and describe the data to evaluate the extensive resources of the data.

We can explain data using different methods. Statistics, the mathematics branch, assist us in collecting, organizing, visualizing, and interpreting data. Also, statistics are divided into two kinds: descriptive and inferential. Inferential and descriptive statistics are dependent on the same data set. Descriptive statistics only depend on this dataset, although inferential statistics often rely on this data to generalize the population.

Let’s get deep into this article today to know about descriptive statistics.

What is Descriptive Statistics?

As the name implies, descriptive statistics describe data. It is a tool for gathering, organizing, summarizing, showing, and analyzing samples from a population. Descriptive statistics do not depend on probability theory, unlike inferential statistics. It leads the way for better understanding and viewing of data.

Descriptive statistics are precious because it would be challenging to imagine what the data shows if we just presented our raw data, mainly if there are many. Therefore, descriptive statistics allow us to contribute the data more meaningfully and make it easier to interpret it.

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Purpose of Descriptive Statistics

The essential features of the data of a study are defined using descriptive statistics. The sample and measurements are summarized. They form the basis of practically any quantitative analysis of the data in combination with simple graphic analysis. Also, a data set can be summarised and described using descriptive statistics through a variety of tabulated and graphical explanations and discussion of the observed results. Complex quantitative data are summed up in descriptive statistics. For two purposes, descriptive statistics can be helpful to 1) providing essential data on variables in a dataset and 2) highlighting possible relationships between variables.

Different types of Descriptive Statistics

1. Frequency Distribution

The frequency distribution is used for quantitative and qualitative data, showing the frequency or count of the different results in a data set or sample. The frequency distribution is usually displayed in a table or map. In the table, each entry or graph is associated with the number or frequency of values occurring at an interval, range, or particular category.

Frequency distribution is essentially a summary of clustered data classified by mutually exclusive classes and the number of occurrences in each category. It enables a more streamlined and systematic way of presenting raw data.

The frequency distribution and visualization charts and graphs commonly used include bar diagrams, histograms, charts, and line diagrams.

2. Central Tendency

Central tendency refers to a descriptive overview of a data set that uses a single value that reflects the data distribution center. Mean, median, and mode are simple measures of central tendency, known as central location measures.

The most famous indicator of the central tendency is the average or the most common value in a data set. The median refers to the middle score for an ascending order of results.

3. Variability

A variability measurement is descriptive statistics showing the extent of dispersion in a sample. The variability measurements decide how far from the middle the data points seem to fall.

Dispersion, spread, and variability each apply to and indicate the range and width of the values’ distribution in a data set. The range, standard deviations, and variances show the various components and aspects of the spread.

The range shows the dispersion level or the optimal distance of the maximum to the lowest values in a data set. The standard deviation is applied to calculate the average variance in a data set and gain insight into the distance or discrepancy between a value in a data set and its mean value. The variance shows the extent of the spread and is an overall average of the squared deviations.

Examples for Descriptive Statistics

1. If you want a clear example of descriptive statistics, look only at the student’s grade point average (GPA). A GPA collects data points from a wide range of grades, classes, and tests, then averages them, and finally provides an overall view of the student’s average academic performance. Note that the GPA does not forecast or produce any conclusions for future results. It gives instead an unambiguous explanation of the academic achievement of students based on data values.

2. Take a simple number to sum up how well a batter does in baseball, the average batting. This figure is just the number of hits divided by the number of times at bat (stated as three significant digits).

Limitations of Descriptive Statistics

Descriptive statistics are so small that only the individuals or items you have calculated are summed up. The data you have obtained cannot be used to generalize to others or objects (i.e., to use data from a sample to determine the properties/parameters). When testing a drug to beat cancer in your patients, for example, you cannot say it would operate only based on descriptive statistics in other cancer patients (whereas inferential statistics will give you this opportunity).

Summarizing lines

• The word “descriptive statistics” refers to the analysis, synthesis, and presentation of results relating to a sample or whole population data set.

• Descriptive statistics aid the analysis of data. It permits a meaningful and intelligible presentation of data, thereby allowing a simplified understanding of the data set.