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# Descriptive and Inferential statistics ## What is statistics?

Statistics is a branched subject from mathematics. It is a science that focuses mainly on the collection, organization, analysis, interpretation, and presentation of data. When we talk about statistical analysis, there are two concepts that hold vital importance in this field. These are Descriptive Statistics and Inferential Statistics. In brief, Descriptive statistics analyze the big data with the help of charts and tables. It never attempts to use a sample to reach a conclusion.

On the contrary, in Inferential statistics, researchers test the hypothesis. He/she studies the sample and reaches the conclusions of the population.

This blog is based on Descriptive and Inferential statistics. It will help you to understand Descriptive and Inferential statistics in detail. You will be going to read the following things in this blog:

• Descriptive statistics definition
• Inferential statistics definition
• Difference between descriptive and inferential statistics
• Role of statistical software in data analysis

So, let us begin reading about descriptive and inferential statistics in detail.

## Descriptive statistics definition

Descriptive statistics are small constants that help in summarizing or briefing the data set. This data set can be entire or a sample of a given population. Descriptive statistics are bifurcated into measures of central tendency and measures of spread or variability.

Measures of central tendency have meant median and mode. It helps in describing the central position of a frequency distribution for a given raw data. Various measures of central tendency are preferable to use in different situations and conditions.

• Mean: This has used during continuous data. It is also known as the arithmetic average.
• Median: Median splits the data into two equal parts. One half of the data is smaller than that number, the other half is larger. Median can be used for continuous or ordinal data.
• Mode: Mode has a large sum of data. It is used for categorical data.

To know more about descriptive statistics refer here.

#### Following measures are used to describe groups in descriptive statistics:

• Central tendency: With the help of mean, mode and median center of the data set can be determined in this. It provides a descriptive summary of the complete data set with a single value.
• Dispersion: One can extend the data in descriptive statistics by measuring the dispersion. With the help of the range of standard deviation and variance, one can measure the dispersion.
• Skewness: Skewness refers to the distortion in a bell curve, or in a normal distribution. One can identify whether the distribution of the values is in symmetry or skewed.

## Inferential statistics definition

To get a clear view of the term inferential statistics, one needs to seek the understanding of the term population in statistics first. When one says the population in statistics, it does not only imply the human population only. Rather, it means entire raw data for the analysis. There are times when one has to analyze the data that is not present completely. Here one has to use samples for data analysis. For instance, you want to collect the data on the number of cancer patients below 18 years of age all over the world. Here you may not find the exact number of patients. Here you use the sample data for a specific population.

In such cases, inferential statistics are having techniques that allow us to make the proper utilization of the samples to do inductive reasoning about the population data. Here the researcher can reach a generalized conclusion. Researchers use the sampling process for the representation of the population as nearly as possible. Data science is a subject where one uses inferential statistics in an extensive manner.

The chances of getting complete accuracy are pretty slim in the process of sampling. Sometimes sampling can lead to errors and discrepancies in the interpretation. Therefore, researchers prefer to use inferential statistics.

#### There are two common methods of inferential statistics, these are:

• Parameters estimation: Parameters are descriptive estimates of the complete raw population. They are also known as sample statistics in this one that works on a random sample of the population. In parameter, estimation the researcher finds the estimate of the population with the help of a sample. This estimation is not precise.
• Statistical hypothesis testing: This method of inferential statistics lets us draw conclusions for the complete or whole population based on a sample.

## Role of statistical software in data analysis

Statistical software is a tool that is used for statistically analyzing the data collected in the research. Such software make data analysis a hassle-free process. To reduce the time and bring accuracy, many researchers take the help of this software. This software always has a great advantage over the manual analysis of data. Let us see how:

### Reduces the chance of error in sampling

Research is successful only if the collected data is properly analyzed. If data is collected by the faulty procedures, it serves useless and false results. Error in sampling happens when there is a discrepancy in the actual population and the sample population.

When a person uses statistical software, it can access the larger database and provides hassle-free customizations. The software also cut down the workload. Most of the software are automated. You don’t need to input the data manually again and again.

### Simplified solutions with accurate results

The statistical analysis can bring simple answers to any complex problem or question. This could be merit when one has to examine the limited data. But oversimplified solutions could be a demerit when data is in large amounts as it will lead to incorrect analysis.

When one uses statistical software, it helps in generating an easy solution along with accurate results. The software provides features such as multivariate analysis, regression analysis, statistical process control, and various more for easy handling of the data. With the help of these features crucial data remains safe as well as results remain easy to understand.

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