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 StatisticalAnalysis?

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 aboutunderstanding 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:

## FAQs

### What are the different types of the statistical analysis explain briefly? ›

There are two main types of statistical analysis: **Descriptive statistics explains and visualizes the data you have, while inferential statistics extrapolates the data you have onto a larger population**. Statistical analysis can help companies cut costs and improve workplace efficiency, among other benefits.

**What are the 7 steps in the statistical process in order? ›**

**1.2 - The 7 Step Process of Statistical Hypothesis Testing**

- Step 1: State the Null Hypothesis. ...
- Step 2: State the Alternative Hypothesis. ...
- Step 3: Set. ...
- Step 4: Collect Data. ...
- Step 5: Calculate a test statistic. ...
- Step 6: Construct Acceptance / Rejection regions. ...
- Step 7: Based on steps 5 and 6, draw a conclusion about.

**What is statistical analysis provide two examples of statistical analysis methods in your answer? ›**

Statistical analysis is **the science of collecting data and uncovering patterns and trends**. It's really just another way of saying “statistics.” After collecting data you can analyze it to: Summarize the data. For example, make a pie chart.

**What are the types of statistical methods in research? ›**

Two main statistical methods are used in data analysis: **descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics**, which draw conclusions from data using statistical tests such as student's t-test.

**What are the main types of statistics? ›**

Descriptive and Inferential Statistics

The two major areas of statistics are known as descriptive statistics, which describes the properties of sample and population data, and inferential statistics, which uses those properties to test hypotheses and draw conclusions.

**What is data analysis explain in detail? ›**

Data Analysis is **the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data**.

**How do you analyze data give 7 answers? ›**

**In this post, we will show you seven essential steps to analyze your data.**

- Identify Research Questions. ...
- Define Measurement Priorities. ...
- Data Accumulation. ...
- Data Filtering. ...
- Conduct the Analysis. ...
- Interpretation of the Results. ...
- Presentation.

**What are the steps of statistical analysis? ›**

- Step 1: Write your hypotheses and plan your research design. ...
- Step 2: Collect data from a sample. ...
- Step 3: Summarize your data with descriptive statistics. ...
- Step 4: Test hypotheses or make estimates with inferential statistics. ...
- Step 5: Interpret your results.

**Why is statistical analysis useful? ›**

The goal of statistical analysis is **to identify trends**. A retail business, for example, might use statistical analysis to find patterns in unstructured and semi-structured customer data that can be used to create a more positive customer experience and increase sales.

**What are the statistical tools used in data analysis? ›**

Some of the most common and convenient statistical tools to quantify such comparisons are **the F-test, the t-tests, and regression analysis**. Because the F-test and the t-tests are the most basic tests they will be discussed first.

### What is the statistical method used in making decisions using experimental data? ›

**Hypothesis testing** is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter.

**What is the most commonly used statistical method for analyzing data? ›**

**Mean or average** mean is one of the most popular methods of statistical analysis. Mean determines the overall trend of the data and is very simple to calculate. Mean is calculated by summing the numbers in the data set together and then dividing it by the number of data points.

**What is statistics definition and example? ›**

Statistics Definition: Statistics is **a branch that deals with every aspect of the data**. Statistical knowledge helps to choose the proper method of collecting the data and employ those samples in the correct analysis process in order to effectively produce the results.

**How many types of data are there in statistics? ›**

Types of Data in Statistics (**4 Types** - Nominal, Ordinal, Discrete, Continuous)

**How many statistics are there? ›**

Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

**What are the 8 stages of data analysis? ›**

data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...

**What is the analysis process? ›**

Definition: A process analysis is **a systematic review of all steps and procedures followed to perform a given activity**. It is a description of the way a particular task is done within an organization.

**What is data analysis PDF? ›**

**The process of performing certain**. **calculations and evaluation in order to extract**. **relevant information from data** is called data. analysis.

**› Statistics Guides ›**

### Statistical Analysis - Meaning, Methods, Types & Examples

### Statistical Data Analysis

### What is Statistical Analysis? Types, Methods and Examples

**What are the three types of statistical analysis? ›**

**There are three major types of statistical analysis:**

- Descriptive statistical analysis. ...
- Inferential statistical analysis. ...
- Associational statistical analysis. ...
- Predictive analysis. ...
- Prescriptive analysis. ...
- Exploratory data analysis. ...
- Causal analysis. ...
- Data collection.

### What are the 3 types of statistics? ›

They are: **(i) Mean, (ii) Median, and (iii) Mode**. Statistics is the study of Data Collection, Analysis, Interpretation, Presentation, and organizing in a specific way.

**What are the 2 main types of statistics? ›**

The two main branches of statistics are descriptive statistics and inferential statistics. Both of these are employed in scientific analysis of data and both are equally important for the student of statistics.

**Why is a statistical analysis important? ›**

Statistical analysis can **provide valuable information needed to make decisions when introducing new products in the market**. Analysis can be done to establish the reliable markets for the product, and also to predict demand and sales. It can also help in identifying the perfect launch timing.

**What is the importance of statistical analysis in research? ›**

Using appropriate statistics, **you will be able to make sense of the large amount of data you have collected so that you can tell your research story coherently and with justification**. Put concisely, statis- tics fills the crucial gap between information and knowledge.

**What are analysis methods? ›**

Data analysis methods and techniques are useful for finding insights in data, such as metrics, facts, and figures. The two primary methods for data analysis are **qualitative data analysis techniques and quantitative data analysis techniques**.

**What are the 8 descriptive statistics? ›**

Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the **mean, median, and mode, while measures of variability include standard deviation, variance, minimum and maximum variables, kurtosis, and skewness**.

**What is statistics definition and example? ›**

Statistics Definition: Statistics is **a branch that deals with every aspect of the data**. Statistical knowledge helps to choose the proper method of collecting the data and employ those samples in the correct analysis process in order to effectively produce the results.

**What are the 4 basic elements of statistics? ›**

The Five Basic Words of Statistics

The five words **population, sample, parameter, statistic (singular), and variable** form the basic vocabulary of statistics. You cannot learn much about statis- tics unless you first learn the meanings of these five words.

**How many types of data are there in statistics? ›**

Types of Data in Statistics (**4 Types** - Nominal, Ordinal, Discrete, Continuous)

**What are the types of data? ›**

4 Types of Data: Nominal, Ordinal, Discrete, Continuous.