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The collection, organization, and analyses of data are called Statistics. The science of interpretation and presentation of the data analyzed is the prime objective of statistics.
The statistical analysis is applied in many industries and many countries. It helps in identifying an ongoing or new future problem that can be subdued.
Statistics include all the aspects of information collection. It can be through surveys, population count, and in many ways.
There are two important types of statistical methods are used widely. One is the descriptive statistics and the other is the inferential statistics.
These two combines together offer a lot of advantages in identifying future needs. Statistics stand base for many informed decisions taken by a government or an organization.
Descriptive Statistics and Inferential Statistics help in concluding a lot of issues that must be addressed. The best part about statistics is, it addresses future needs.
Descriptive vs Inferential Statistics
The difference between descriptive and inferential statistics is the way it looks at data. The descriptive statistics describe the population whereas inferential statistics take a sample of people for a particular pattern and generalizes it with the whole lot.
Comparison Table Between Descriptive and Inferential Statistics (in Tabular Form)
Parameter of Comparison  Descriptive  Inferential 

Meaning/Definition  Descriptive Statistics is the branch of statistics that describes the population.  Inferential Statistics is the branch of statistics that concludes the entire population by studying a sample percentage of people in the population. 
Nature of Work  Descriptive statistics organizes, analyses, and presents data in a perfectly meaningful way for more investigation.  Inferential statistics is all about comparison of data and prediction from the analyzed data to conclude. 
Final Result  Descriptive statistics offers graphs, Charts, and also tables.  Inferential Statistics gives the probability of a particular occurrence inferred from the data. 
Main Usage  Descriptive statistics describe the situation understudy  Inferential statistics clearly explains the future occurrence of an event by chances. 
Main Functionality  Descriptive statistics normally gives data which everyone knows. It just summarizes it.  Inferential statistics reaches beyond the data. It gives conclusions about the population under study and it helps in learning the population behavior percentagewise. 
What is Descriptive Statistics?
Descriptive statistics is the branch of statistics that helps describe the population under study. The important characteristics of the dataset are quantitatively described by descriptive statistics.
The description happens through certain properties like mean, median, mode, and also measures of dispersion. The descriptive statistics provide the information in a meaningful way utilizing graphs, charts, and tables.
The data is mentioned accurately too. The information may also contain a few diagrams which will be explained in the same context.
Descriptive statistics offer simple information about the sample in the study. This forms the first phase of data analysis for a huge statistical analysis.
Descriptive statistics are extensively used in the business world to procure some useful data. The dataset from which the statistical analysis is carried out fetches a lot of information that is already known to everyone, but it is presented in a meaningful impact it created to a certain situation.
At times, the sample dataset may have two to three variables. In that case, descriptive statistics are bound to give the relationship among all the three variables. There are indeed three types of analysis; univariate, bivariate, and multivariate analysis.
The dataset f it has one variable then the analysis is called univariate, if it has two or more then it will be bivariate or multivariate analysis.
What is Inferential Statistics?
Inferential statistics is the branch of statistics that concludes by analyzing a sample from a whole lot of a particular pattern. Inferential statistics is generalizing a particular fact to the whole lot by examining a sample of it.
The deduction of the result from the sample is judged the same for the whole group. It is indeed a very convenient way when a large number of numbers or population cannot be examined for a particular cause.
The sample chosen must be exactly from the whole lot and the result of the sample will directly apply to the whole lot. Mostly, inferential statistics work with probability theory.
The methods used in inferential statistics is the estimation of parameters and testing of hypothesis. The propositions made out of the sample become models and the same model is subjected to the entire community.
The models of inferential statistics are many, it involves the approximate distribution of the sample data. But all the models arrive at one particular conclusion but used in different scenarios. Inferential analysis can be used to check what the entire population might think about the new government. This can happen just by checking with a few thousands of people
Main Differences Between Descriptive and Inferential Statistics
 Though both the branch of statistics is widely important, they have their differences. The main difference between Descriptive Statistics and Inferential Statistics is, the descriptive statistics describe the population while the inferential statistics help learn the population by examining a sample of it. It helps in concluding by analyzing the data of a sample of people.
 Descriptive statistics is brilliant in providing the data in a meaningful way through graphical representations, charts, and tables while inferential statistics help in comparing and predicting from the data.
 The final result of descriptive statistics can be a diagram or a graphical representation but inferential statistics offer the probability of a particular occurrence.
 Descriptive statistics help in describing a situation while inferential statistics explain the chance of a future event occurrence.
 Descriptive statistics give the data which is already known, but inferential statistics conclude learning about the population. This is sometimes beyond the numbers projected.
Conclusion
The statistical analysis is a perfect way to find solutions to many ongoing as well as future problems. Though the descriptive statistics offer only direct data which may be already known. How it is presented gives way to many explorations of information.
This exploration gives rise to inferential statistics. The inferential statistics gives wonderful conclusions about a lot of facts. This is an easier way to arrive at a conclusion where the numbers available are larger than larger.
The focus on making assumptions in inferential statistics is also logical, so mistakes are a matter of probability. Which one to use when the researchers know it very well.
References
 https://repository.upenn.edu/cgi/viewcontent.cgi?article=1314&context=marketing_papers
 https://journals.library.ualberta.ca/eblip/index.php/EBLIP/article/view/168
 https://psycnet.apa.org/record/199498130000
 https://arxiv.org/abs/1302.2525
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