10 Different Types of Statistical Analysis (Complete Guide)

In statistics, there are so many different types of analysis which are needed if you really want to understand your data correctly. But where do you start, given there are so many different methods of statistical analysis to choose from?

Ultimately, each type has its own unique set of advantages and disadvantages, and it’s really important to understand these if you want to choose the right one for your specific data set.

Well that’s exactly what we’ll cover here, plus we’ll throw in some useful tips which will help you decide on which statistical analysis method is best suited for your specific needs.

Ready to get started? Let’s dive in!

Table of contents
Types of Statistical Analysis
1. Descriptive statistics
2. Inferential Statistics
3. Exploratory Data Analysis
4. Predictive Analytics
5. Prescriptive Analysis
6. Causal Analysis
7. Text Analytics
8. Mechanistic Analysis
9. Associational statistical analysis
10. Social Network Analysis

Types of Statistical Analysis

There are many different types of statistical analysis which can help to give valuable insights into your data. Here are 10 in particular which are worth noting.

1. Descriptive statistics

Descriptive statistics can be used to describe the basic features of a specific data set. With a focus on measuring the central tendencies, such as the mean and median, and measures of dispersion, such as the standard deviation and range.

Additionally, descriptive analysis is great for creating data visualisations, particularly traditional graphs, such as histograms and box plots.

One of the drawbacks however, is that descriptive statistical analysis doesn’t allow for inferences to be drawn. For example, from a population group, however can still be very useful in providing a general overview of the data.

As such, this method is absolutely essential in any data analyst’s toolkit.

2. Inferential Statistics

Inferential statistics is particularly useful when working with smaller data sets, where we want to draw at least some statistically reliable conclusions or possibly even make projections.

This is done by extrapolating data from a smaller sample to infer or estimate the characteristics of a wider population.

Inferential statistical analysis is used in a wide range or fields, everything from medicine to marketing.

For example, if a drug company wants to assess how effective a new medication will be, they can use inferential statistics to test a small sample group of people and then extrapolate the results to the larger population.

In general, inferential statistics is used when we want to make predictions or generalizations about a large group of people based on data from a smaller group.

3. Exploratory Data Analysis

One of the most important things in data science is exploratory data analysis. This helps you understand the data better and find patterns.

There are many techniques of exploratory data analysis, but some of the most common are visualization and summary statistics.

• Visualization is a way of looking at data that can help you find patterns. Summary statistics are another way of understanding data. They involve finding the mean, median, mode, and standard deviation.
• Exploratory data analysis is a very important part of data science because it helps you understand the data better. Without it, you would not be able to find patterns or understand the data as well.

EDA differs from initial data analysis (IDA), focusing more on checking assumptions and preparing the data for modelling. IDA sometimes includes exploratory steps such as generating a new variables, but its main goal is still modelling, not discovery.

EDA is also different from confirmatory data analysis (CDA), which focuses on processing, transforming, and modelling the data to uncover information that supports or refutes hypotheses about how the process or system generated the data operates.

CDA generally begins with a specific hypothesis or predictive model in mind and uses statistical methods to test whether this model accurately describes or predicts the behavior of interest in the population of interest as measured by the data.

EDA focuses on discovering structure in data where there may be none, summarizing main features using appropriate visual methods, and assessing underlying assumptions if a formal model is to fit the data.

Even when a formal modelling approach will eventually be used to make accurate predictions or inferences, exploratory data analysis may still be useful for better understanding the data set or verifying aspects of the initial data production process.

4. Predictive Analytics

Another type of data analysis is Predictive analytics. It uses statistical techniques to make predictions about future events.

This approach is often used in business to forecast sales, identify trends, and make decisions about marketing and operations. Predictive analytics can be used on various data sources, including historical, customer, financial, and environmental data.

Predictive analytics aims to build models that can accurately predict the likelihood of future events and provide decision-makers with actionable insights. It typically employs a range of statistical and machine learning methods to achieve this goal.

Common examples include:

• regression
• time series
• decision trees
• neural networks

However, it’s worth noting that predictive analytics is still an evolving field, and new methods are constantly being developed.

As the availability of data increases and computing power becomes more accessible, the potential applications of predictive analytics are likely to continue to grow.

5. Prescriptive Analysis

Prescriptive Analytics is a branch of statistics that deals with making predictions about future events.

This type of analysis differs from descriptive and predictive analytics, which describe past behavior and make predictions about future behavior based on past data.

Prescriptive analytics takes things one step further by not only predicting what will happen but also providing recommendations about what should be done to achieve the desired outcome.

To do this, prescriptive analytics uses optimization techniques to find the best possible solution given a set of constraints. As such, prescriptive analytics can solve various problems, ranging from financial planning to logistics.

While prescriptive analytics is still in its early stages of development, it has the potential to revolutionize the way businesses operate by providing them with a powerful tool for making decisions.

6. Causal Analysis

Causal analysis is a data analysis used to identify the cause-and-effect relationships between variables. This approach is often used in business and research to identify factors contributing to a particular outcome.

Causal analysis typically employs various statistical methods, including regression, time series, and experimental design.

The goal of causal analysis is to find the causal factors that influence a particular outcome so that these factors can be controlled or manipulated to achieve the desired result.

Causal analysis is important for understanding the world and making better decisions.

7. Text Analytics

Text analytics is data analysis used to extract insights from textual data. This approach is often used in business to understand customer sentiment, extract topics of interest, and make decisions about marketing and product development.

Text analytics can be used on various data sources, including social media data, customer reviews, survey responses, and web page content.

The goal of this is to turn unstructured text data into actionable insights by employing a range of natural language processing techniques.

Some common examples include text classification:

• topic modeling
• sentiment analysis
• and entity extraction

Text analytics is an evolving field, and new methods are constantly being developed. As the availability of data increases and computing power becomes more accessible, the potential applications of text analytics are likely to continue to grow.

8. Mechanistic Analysis

The mechanistic analysis is a statistical analysis used to identify complex systems’ causal mechanisms. This approach is often used in research to understand how a system works and make predictions about how it will behave in the future.

Mechanistic analysis typically employs various mathematical and statistical methods, including differential equations, dynamical systems theory, and network analysis.

The mechanistic analysis aims to find the underlying causes of a system’s behavior so that these causes can be controlled or manipulated to achieve the desired result.

In some cases, mechanistic analysis can be used to understand the behavior of natural systems.

The mechanistic analysis is an important tool for understanding the world and making better decisions.

9. Associational statistical analysis

Associational statistical analysis is a type of statistical analysis that is used to identify the relationships between variables. This approach is often used in business and research to understand the factors contributing to a particular outcome.

Associational analysis typically employs various statistical methods, including regression, factor, and cluster analysis. The associational analysis aims to find the underlying relationships between variables so that these relationships can be exploited / controlled.

In some cases, associational analysis can identify the causes of complex problems.

Associational statistical analysis is important for understanding the world and making better decisions.

10. Social Network Analysis

Social network analysis is a type of data analysis used to examine the relationships between people. This approach is often used in business to understand how customers interact with each other and make predictions about future behavior.

It can be used on various data sources, including customer, financial, and social media data and aims to find patterns in data that can be used to make reliable predictions.

This form of analysis typically employs a range of statistical and machine learning techniques to achieve this goal like others above.

As data becomes more available and computing power becomes more accessible, the potential applications of social network analysis are likely to continue to grow.

How to Choose the Right Statistical Analysis?

The type of statistical analysis you use will depend on the type of data that you have and the question you want to answer. In some cases, multiple types of statistical analysis may be appropriate. In other cases, only one type of analysis will be appropriate.

Here are a few things to consider when choosing the right statistical analysis:

1. The type of data you have

Some types of data are more suited to certain types of statistical analysis than others. For example, if you have categorical data, then a chi-square test may be more appropriate than a t-test.

2. The question you want to answer

Different types of statistical analysis are better suited to different questions. For example, if you want to know whether two groups are different from each other, then a t-test may be more appropriate than regression analysis.

3. The complexity of the question

Some questions are more complex than others and may require multiple types of statistical analysis. For example, if you want to know how two variables are related, a correlation analysis may be followed by a regression analysis.

4. The assumptions you can make

Some types of statistical analysis require certain assumptions about the data. For example, t-tests assume that the data is normally distributed. If this assumption is not met, another type of test may be more appropriate.

5. The amount of data you have

Some types of statistical analysis require more data than others. For example, regression analysis typically requires more data than a t-test.

6. The resources you have

Some types of statistical analysis are more computationally intensive than others. For example, a Monte Carlo simulation may be more appropriate if you can access a powerful computer.

7. The level of expertise you have

Some types of statistical analysis are more complex than others. For example, Multiple imputations may be more appropriate if you have a statistics degree.

The type of statistical analysis that you use will also depend on the software that you are using. Some software packages are better suited to certain types of statistical analysis than others.

For example, SPSS is a popular statistics software package used for regression analysis. However, R is a more powerful statistical programming language often used for more complex analyses, such as Monte Carlo simulations.

When choosing the right statistical analysis, it is important to consult with a statistician or data analyst. They will be able to help you choose the right analysis for your data and question.

Summary

Statistical analysis is a process of understanding data by applying mathematical and statistical techniques. There are many different types of statistical analysis, each with its own strengths and weaknesses.

The type of statistical analysis that you use will depend on the type of data that you have, and the question that you want to answer.

So now, hopefully you’ll know exactly which statistical analysis you should be using to get the most our of your data!

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