Statistics science is used widely in so many areas such as market research, business intelligence, financial and data analysis and many other areas.

Why? Simply because statistics is a core basis for millions of business decisions made every day.

The two main **types of statistical analysis** and methodologies are descriptive and inferential. However, there are other types that also deal with many aspects of data including data collection, prediction, and planning.

On this page:

- What is statistical analysis? Definition and explanation.
- What are the different types of statistics?

(descriptive, inferential, predictive, prescriptive, exploratory data analysis and mechanistic analysis explained) - An infographic in PDF for free download.

## What is Statistical Analysis?

First, let’s clarify that “statistical analysis” is just the second way of saying “statistics.” Now, the official definition:

Statistical analysis is a study, a science of **collecting**, organizing, exploring, interpreting, and presenting data and uncovering **patterns and trends**.

Many businesses rely on statistical analysis and it is becoming more and more important. One of the main reasons is that statistical data is used to predict future trends and to minimize risks.

Furthermore, if you look around you, you will see a huge number of products (your mobile phone for example) that have been improved thanks to the results of the statistical research and analysis.

Here are some of the fields where statistics play an important role:

- Market research, data collection methods, and analysis
- Business intelligence
- Data analysis
- SEO and optimization for user search intent
- Financial analysis and many others.

Statistics allows businesses to dig deeper into specific information to see the current situations, the future trends and to make the most appropriate decisions.

There are two key types of statistical analysis: descriptive and inference.

### The Two Main Types of Statistical Analysis

In the real world of analysis, when analyzing information, it is normal to use both descriptive and inferential types of statistics.

Commonly, in many research run on groups of people (such as marketing research for defining market segments), are used both descriptive and inferential statistics to analyze results and come up with conclusions.

What is descriptive and inferential statistics? What is the difference between them?

**Descriptive Type of Statistical Analysis**

As the name suggests, the descriptive statistic is used to describe! It describes the basic features of information and shows or summarizes data in a rational way. Descriptive statistics is a study of quantitatively describing.

This type of statistics draws in all of the data from a certain population (*a population is a whole group, it is every member of this group*) or a sample of it. Descriptive statistics can include numbers, charts, tables, graphs, or other data visualization types to present raw data.

However, descriptive statistics do not allow making conclusions. **You can not get conclusions** and make generalizations that extend beyond the data at hand. With descriptive statistics, you can simply describe what is and what the data present.

**For example**, if you have a data population that includes 30 workers in a business department, you can find the average of that data set for those 30 workers. However, you can’t discover what the eventual average is for all the workers in the whole company using just that data. Imagine, this company has 10 000 workers.

Despite that, this type of statistics is very important because it allows us to show data in a meaningful way. It also can give us the ability to make a simple interpretation of the data.

In addition, it helps us to simplify large amounts of data in a reasonable way.

**Inferential Type of Statistical Analysis**

As you see above, the main limitation of the descriptive statistics is that it only allows you to make summations about the objects or people that you have measured.

It is a serious limitation. This is where inferential statistics come.

Inferential statistics is a result of more complicated mathematical estimations, and allow us to infer trends about a larger population based on samples of “subjects” taken from it.

This type of statistical analysis is used to study the relationships between variables within a sample, and you can make conclusions, generalizations or predictions about a bigger population. In other words, the sample accurately represents the population.

Moreover, inference statistics allows businesses and other organizations to **test a hypothesis and come up with conclusions** about the data.

One of the key reasons for the existing of inferential statistics is because it is usually too costly to study an entire population of people or objects.

To sums up the above two main types of statistical analysis, we can say that descriptive statistics are used to describe data. Inferential statistics go further and it is used to infer conclusions and hypotheses.

### Other Types of Statistics

While the above two types of statistical analysis are the main, there are also other important types every scientist who works with data should know.

**Predictive Analytics**

If you want to make predictions about future events, predictive analysis is what you need. This analysis is based on current and historical facts.

Predictive analytics uses statistical algorithms and machine learning techniques to define the likelihood of future results, behavior, and trends based on both new and historical data.

Data-driven marketing, financial services, online services providers, and insurance companies are among the main users of predictive analytics.

More and more businesses are starting to implement predictive analytics to increase competitive advantage and to minimize the risk associated with an unpredictable future.

Predictive analytics can use a variety of techniques such as data mining, modeling, artificial intelligence, machine learning and etc. to make important predictions about the future.

It is important to note that no statistical method can “predict” the future with 100% surety. Businesses use these statistics to answer the question “**What might happen?**“. Remember the basis of predictive analytics is based on probabilities.

**Prescriptive Analytics**

Prescriptive analytics is a study that examines data to answer the question “**What should be done?**” It is a common area of business analysis dedicated to identifying the best movie or action for a specific situation.

Prescriptive analytics aims to find the optimal recommendations for a decision making process. It is all about providing advice.

Prescriptive analytics is related to descriptive and predictive analytics. While descriptive analytics describe what has happened and predictive analytics helps to predict what might happen, prescriptive statistics aims to find the best options among available choices.

Prescriptive analytics uses techniques such as simulation, graph analysis, business rules, algorithms, complex event processing, recommendation engines, and machine learning.

**Causal Analysis**

When you would like to understand and identify the reasons why things are as they are, causal analysis comes to help. This type of analysis answer the question** “Why?”**

The business world is full of events that lead to failure. The causal seeks to identify the reasons why? It is better to find causes and to treat them instead of treating symptoms.

Causal analysis searches for the root cause – the basic reason why something happens.

Causal analysis is a common practice in industries that address major disasters. However, it is becoming more popular in the business, especially in IT field. For example, the causal analysis is a common practice in quality assurance in the software industry.

So, let’s sum the goals of casual analysis:

- To identify key problem areas.
- To investigate and determine the root cause.
- To understand what happens to a given variable if you change another.

**Exploratory Data Analysis (EDA)**

Exploratory data analysis (EDA) is a complement to inferential statistics. It is used mostly by data scientists.

EDA is an analysis approach that focuses on identifying general patterns in the data and to find** previously unknown relationships**.

The purpose of exploratory data analysis is:

- Check mistakes or missing data.
- Discover new connections.
- Collect maximum insight into the data set.
- Check assumptions and hypotheses.

EDA alone should not be used for generalizing or predicting. EDA is used for taking a bird’s eye view of the data and trying to make some feeling or sense of it. Commonly, it is the first step in data analysis, performed before other formal statistical techniques.

**Mechanistic Analysis**

Mechanistic Analysis is not a common type of statistical analysis. However it worth mentioning here because, in some industries such as big data analysis, it has an important role.

The mechanistic analysis is about understanding the exact changes in given variables that lead to changes in other variables. However, mechanistic does not consider external influences. The assumption is that a given system is affected by the interaction of its own components.

It is useful on those systems for which there are very clear definitions. Biological science, for example, can make use of.

Download the following infographic in PDF: 7 Key Types of Statistical Analysis:

Wonderful read. Such a useful and very interesting stuff to do in every research and data analysis you wanna do! Thank you very much for the very organized data analysis tips I learned a lot from it. I really loved this write up, You Nailed It.

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