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7 Types of Statistical Analysis: Definition and Explanation

  • Neelam Tyagi
  • Jan 19, 2021
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Statistics is the branch of science that renders various tools and analytical techniques in order to deal with the huge extent of data, in simple terms, it is the science of assembling, classifying, analyzing and interpreting & manifesting the numeric form of data for making inferences about the population, from the picked out sample data that can be used by business experts to solve their problems.

 

Therefore, in the efforts to organize data and anticipates future trends, depending upon the information, many organizations heavily rely on statistical analysis.

 

More precisely, statistical data analysis concerns data collection, interpretation and presentation. It can be approached while handling data to solve complex problems. More precisely, the statistical analysis delivers significance to insignificant/irrelevant data or numbers. 


 

The Key types of Statistical Analysis are

 

In particular, statistical analysis is the process of consolidating and analyzing distinct samples of data to divulge patterns or trends and anticipating future events/situations to make appropriate decisions. 

 

The statistical analysis has the following types that considerably depends upon data types. 


Displaying seven types of statistical analysis. i.e, descriptive and inferential statistical analysis, predictive, prescriptive analysis, exploratory data analysis, causal and mechanistic analysis.

7 types of statistical analysis


1. Descriptive Statistical Analysis

 

Fundamentally, it deals with organizing and summarizing data using numbers and graphs. It makes easy the massive quantities of data for intelligible interpretation even without forming conclusions beyond the analysis or responding to any hypotheses. 

 

Instead of processing data in its raw form, descriptive statistical analysis enables us to represent and interpret data more efficiently through numerical calculation, graphs or tables.

 

From all necessary preparatory steps to concluding analysis and interpretation, descriptive statistical analysis involves various processes such as tabulation, a measure of central tendency (mean, median, mode), a measure of dispersion or variance (range, variation, standard deviation), skewness measurements and time-series analysis.


Displaying the formula chart of mean, median, deviation, variance, and many more.

Formula chart, source


Under descriptive analysis, the data is summarized in tabular form and managed & presented in the forms of charts and graphs for summing up data, assuming it for the whole population. 

 

(Most related: Descriptive statistics in R)

 

Moreover, it helps in extracting distinct characteristics of data and in summarizing and explaining the essential features of data. What’s more, no insights are drawn regarding the groups which are not observed/sampled.

 

2. Inferential Statistical Analysis

 

The inferential statistical analysis basically is used when the inspection of each unit from the population is not achievable, hence, it extrapolates, the information obtained, to the complete population. 

 

In simple words, inferential statistical analysis lets us test a hypothesis depending on a sample data from which we can extract inferences by applying probabilities and make generalizations about the whole data, and also can make conclusions with respect to future outcomes beyond the data available.

 

By this way, it is highly preferable while drawing conclusions and making decisions about the whole population on the basis of sample data. As such, this method involves the sampling theory, various tests of significance, statistical control etc.


Explaining the difference between descriptive and inferential statistical analysis.

Descriptive vs Inferential Statistical Analysis 


3. Predictive Analysis

 

Predictive analysis is implemented to make a prediction of future events, or what is likely to take place next, based on current and past facts and figures. 

 

In simple terms, predictive analytics uses statistical techniques and machine learning algorithms to describe the possibility of future outcomes, behaviour, and trends depending on recent and previous data. Widely used techniques under predictive analysis include data mining, data modelling, artificial intelligence, machine learning and etc. to make imperative predictions. 

 

In the current business system, this analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations, however, any business can take advantage of it by planning for an unpredictable future, such as to gain the competitive advantage and narrow down the risk connected with an unpredictable future event.

 

The predictive analysis converges on forecasting upcoming events using data and ascertaining the likelihood of several trends in data behaviour. Therefore, businesses use this approach to get the answer “what might happen?” where the basis of making predictions is a probability measure.

 

 

4. Prescriptive Analysis

 

The prescriptive analysis examines the data In order to find out what should be done, it is widely used in business analysis for identifying the best possible action for a situation. 

 

While other statistical analysis might be deployed for driving exclusions, it provides the actual answer. Basically, it focuses on discovering the optimal suggestion for a process of decision making.

  

Several techniques, implemented under prescriptive analysis are simulation, graph analysis, algorithms, complex event processing, machine learning, recommendation engine, business rules, etc.

  

However, it is nearly related to descriptive and predictive analysis, where descriptive analysis explains data in terms of what has happened, predictive analysis anticipates what could happen, and here prescriptive analysis deals in providing appropriate suggestions among the available preferences.


 

5. Exploratory Data Analysis (EDA)

 

Exploratory data analysis, or EDA as it is known, is a counterpart of inferential statistics, and greatly implemented by data experts. It is generally the first step of the data analysis process that is conducted prior to any other statistical analysis techniques.

 

EDA is not deployed alone for predicting or generalizing, it renders a preview of data and assists in getting some key insights into it. 

 

This method fully focuses on analyzing patterns in the data to recognize potential relationships. EDA can be approached for discovering unknown associations within data, inspecting missing data from collected data and obtaining maximum insights, examining assumptions and hypotheses. 


 

6. Causal Analysis

 

In general, causal analysis assists in understanding and determining the reasons behind “why” things occur, or why things are as such, as they appear. 

 

For example, in the present business environment, many ideas, or businesses are there that get failed due to some events’ happening, in that condition, the causal analysis identifies the root cause of failures, or simply the basic reason why something could happen. 

 

In the IT industry, this is used to check the quality assurance of particular software, like why that software failed, if there was a bug, a data breach, etc, and prevents companies from major setbacks.  

 

We can consider the causal analysis when;

  • Identifying significant problem-areas inside the data,
  • Examining and identifying the root causes of the problem, or failure, 
  • Understanding what will be happening to a provided variable if one another variable changes.

 

 

7. Mechanistic Analysis

 

Among the above statistical analysis, mechanistic is the least common type, however, it is worthy in the process of big data analytics and biological science. It is deployed to understand and explain how things happen rather than how specific things will take place ulteriorly.

 

It uses the clear concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences and considering the assumption that the entire system gets influenced via its own internal elements’ interaction.

 

The fundamental objectives of mechanistic analysis involve;

  • Understanding the definite changes in that could make changes in other variables
  • A clear explanation of the happening of a past event in the context of data, especially when the particular subject/concern deals with specific activities.

 

For example, in biological science, when studying and inspecting how various parts of the virus are affected by making changes in medicine. 

 

Besides the above statistical analysis types, it is worth discussing here that these statistical treatments, or statistical data analysis techniques, profoundly rely on the way, the data is being used. While counting on the function and requirement of a particular study, data and statistical analysis can be employed for many purposes, for example, medical scientists can use a variety of statistical analysis for testing the drug effectiveness, or potency.

 

What’s more, plenty of available data can inform numerous things, data professionalists want to explore, therefore statistical analysis is able to get some informative outcomes and make some inferences. Also, in some cases, statistical analysis can be approached to accumulate information regarding the preference of people and their habits. 

 

For example, user data, at sites like Facebook and Instagram, can be used by analysts for understanding user perception, like what uses are doing and what motivates them. This information can benefit commercial ads where a particular group of users are targeted to sell them things. It is also helpful for the application developers to understand users’ response and habits and make changes in products accordingly.


 

Conclusion

 

A deeper understanding of data can widen the numerous opportunities for a business, with the implementation of business analytics, an organization can achieve while scrutinizing data, for driving, for example, predictions, insights, or conclusions from data and this is what statistical analysis can do, for example;

 

  • Compiling and manifesting data in the form of graphs or charts to show key findings,
  • Exploring significant elements/ measurements from data, like mean, variance, skewness, etc,
  • Testing a hypothesis from multiple experiments,
  • Anticipating coming foresight on the basis of past data behaviour, and many more.

 

And hence, a business can take advantages of statistical analysis in various ways, for example, to determine the down performance of sales, to uncover trends from customer data, conducting financial audits, etc.  

 

(Check out my other blog on Bayesian Statistics)

 

We have seen, preferably, two main types “descriptive and inferential statistical analysis” is to choose while applying statistical analysis to a business problem, however, other types of statistical analysis will become a priority while addressing some other business needs, organizations are looking for, depending on the complete intent or queries.

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