Introduction
Descriptive statistics means data analysis for which users take SPSS help. First of all, users collect sample data and then translate it using graphs, tables, and charts. Here users prefer to take a small sample data size because, with the large population of data, quantitative analysis becomes difficult. The data set that you use here is well-formatted and structured. Here you try to establish a relation between two data variables which is further followed by inferential statistics. These inferential statistics determine if the conclusion is true for the sample data set or not. In this blog post, we will know about descriptive statistics, its types, examples, and its comparison with inferential statistics. Let us read the blog further without wasting time.
What are descriptive statistics?
Statistics extract meaningful results & information from the raw data. It is basically the science of identifying, collecting, organising, interpreting, and presenting data. This data could be either qualitative or quantitative in nature and statistics make decision-making more comfortable.
Descriptive statistics can only describe and summarise data. On the other hand, inferential statistics find conclusions for the larger population. With the increase in population, the chances of errors also increase which needs to be resolved. In addition, data distortion, missing figures, and recalculation are also a kind of challenges for the researchers. Hence, descriptive statistics come into the light to get help with small sample size and summarise it.
Descriptive statistics is an influential tool that analyses and represents data for analysis and calculation. Because of this property, it is widely used in business, commerce, and the medical industry.
Types of descriptive statistics
Descriptive statistics are classified into the following categories:
- Frequency distribution
There are a variety of ways in which every aspect of a problem can be accounted for in a frequency distribution. These repetitive occurrences are recorded and mentioned in a tabular format which is further used for qualitative and quantitative data analysis.
For instance, a group of friends go to watch a movie. Some friends have already watched the movie, that is, they are watching it a second time. Some friends are watching the same movie more than two times as well. So here if you divide the group of friends on the basis of the number of times the movie was watched then it will denote the frequency distribution among the friends.
Hence, frequency distribution samples are those that are observed for the number of times that the process or data sample has occurred.
- Central tendency
Central tendency includes three calculation methods:
- Mean
- Median
- Mode
As an aggregate of all counts or occurrences within a data set, the results represent the central value. A mean is the average value of all the occurrences, a median is the average value or the middle value of the data sample, and a mode is the most frequently occurring value.
- Variability
Variability describes how dispersed data points are from one another. A range of dispersion and variance is also designed from the highest to the lowest value of the data sample.
For instance, if the lowest number of visits to the same movie is one and the highest value is 4, then the variability creates a range of values and determines how far is each value from the central tendency.
Purpose of descriptive statistics
The main objective of descriptive statistics is to gather information about a data set. For example, hundreds of soccer players get involved in thousands of games. In such situations, descriptive statistics divide a large amount of data into distinct useful pieces of information.
Descriptive statistics vs Inferential statistics
- Descriptive statistics extract information from the raw data and arrange it into a tabular format. Whereas, inferential statistics make assumptions on the basis of collected data.
- Descriptive statistics organises and presents data in a meaningful manner. While inferential statistics make predictions, do data comparisons, and run hypotheses.
- Descriptive analysis is just useful for describing a situation, but inferential statistics experience further to make useful conclusions. Probabilities, possibilities, and event occurrences are predicted using inferential statistics.
- Small data are typically considered in descriptive analyses. On the other hand, the findings are applied to the entire population using inferential statistics.
- In addition to charts and graphs, researchers use tables and graphs to describe their studies. In the case of inferential statistics, researchers use probability to reach to final conclusions.
Conclusion
Descriptive statistics is a mathematical tool which is used to summarise and & collect data. It represents data in a well-formatted manner and therefore analysts and statisticians around the globe can understand the recorded data. If anyone of you is also facing difficulties with this statistical method then you should choose SPSS data analysis services from professionals. This will make your research work easier. Good luck!
