Top 9 Statistical Tools Used in Research

Well-designed research requires a well-chosen study sample and a suitable statistical test selection. To plan an epidemiological study or a clinical trial, you’ll need a solid understanding of the data. Improper inferences from it could lead to false conclusions and unethical behavior. And given the ocean of data available nowadays, it’s often a daunting task for researchers to gauge its credibility and do statistical analysis on it.

With that said, thanks to all the statistical tools available in the market that help researchers make such studies much more manageable. Statistical tools are extensively used in academic and research sectors to study human, animal, and material behaviors and reactions.

Statistical tools aid in the interpretation and use of data. They can be used to evaluate and comprehend any form of data. Some statistical tools can help you see trends, forecast future sales, and create links between causes and effects. When you’re unsure where to go with your study, other tools can assist you in navigating through enormous amounts of data.

In this article, we will discuss some of the best statistical tools and their key features. So, let’s start without any further ado.

What is Statistics? And its Importance in Research

Statistics is the study of collecting, arranging, and interpreting data from samples and inferring it to the total population. Also known as the “Science of Data,” it allows us to derive conclusions from a data set. It may also assist people in all industries in answering research or business queries and forecast outcomes, such as what show you should watch next on your favorite video app.

Statistics is a technique that social scientists, such as psychologists, use to examine data and answer research questions. Scientists raise a wide range of questions that statistics can answer. Moreover, it provides credibility and legitimacy to research. If two research publications are presented, one without statistics and the other with statistical analysis supporting each assertion, people will choose the latter. 

Statistical Tools Used in Research

Researchers often cannot discern a simple truth from a set of data. They can only draw conclusions from data after statistical analysis. On the other hand, creating a statistical analysis is a difficult task. This is when statistical tools come into play. Researchers can use statistical tools to back up their claims, make sense of a vast set of data, graphically show complex data, or help clarify many things in a short period. 

Let’s go through the top 9 best statistical tools used in research below:

1. SPSS:

SPSS (Statistical Package for the Social Sciences) is a collection of software tools compiled as a single package. This program’s primary function is to analyze scientific data in social science. This information can be utilized for market research, surveys, and data mining, among other things. It is mainly used in the following areas like marketing, healthcare, educational research, etc.

SPSS first stores and organizes the data, then compile the data set to generate appropriate output. SPSS is intended to work with a wide range of variable data formats.

Some of the highlights of SPSS:

  • It gives you greater tools for analyzing and comprehending your data. With SPSS’s excellent interface, you can easily handle complex commercial and research challenges.
  •  It assists you in making accurate and high-quality decisions.
  • It also comes with a variety of deployment options for managing your software.
  • You may also use a point-and-click interface to produce unique visualizations and reports. To start using SPSS, you don’t need prior coding skills.
  •  It provides the best views of missing data patterns and summarizes variable distributions.

2. R

R is a statistical computing and graphics programming language that you may use to clean, analyze and graph your data. It is frequently used to estimate and display results by researchers from various fields and lecturers of statistics and research methodologies. It’s free, making it an appealing option, but it relies upon programming code rather than drop-down menus or buttons. 

Some of the highlights of R:

  • It offers efficient storage and data handling facility.
  • R has the most robust set of operators. They are used for array calculations, namely matrices.
  • It has the best data analysis tools.
  • R’s graphical facilities for data analysis also make it ideal for data visualization .
  • It’s a full-featured high-level programming language with conditional loops, decision statements, and various functions.

3. SAS

SAS is a statistical analysis tool that allows users to build scripts for more advanced analyses or use the GUI. It’s a high-end solution frequently used in industries including business, healthcare, and human behavior research. Advanced analysis and publication-worthy figures and charts are conceivable, albeit coding can be a challenging transition for people who aren’t used to this approach.

Many big tech companies are using SAS due to its support and integration for vast teams. Setting up the tool might be a bit time-consuming initially, but once it’s up and running, it’ll surely streamline your statistical processes.

Some of the highlights of SAS are:

  • It is one of the most user-friendly statistics tools , with a range of tutorials available.
  • Its package includes a wide range of statistics tools.
  • It has the best technical support available.
  • It gives reports of excellent quality and aesthetic appeal
  • It provides the best assistance for detecting spelling and grammar issues. As a result, the analysis is more precise.


MATLAB is one of the most well-reputed statistical analysis tools and statistical programming languages. It has a toolbox with several features that make programming languages simple. With MATLAB, you may perform the most complex statistical analysis, such as EEG data analysis. Add-ons for toolboxes can be used to increase the capability of MATLAB.

Moreover, MATLAB provides a multi-paradigm numerical computing environment, which means that the language may be used for both procedural and object-oriented programming. MATLAB is ideal for matrix manipulation, including data function plotting, algorithm implementation, and user interface design, among other things. Last but not least, MATLAB can also run programs written in other programming languages. 

Some of the highlights of MATLAB:

  • MATLAB toolboxes are meticulously developed and professionally executed. It is also put through its paces by the tester under various settings. Aside from that, MATLAB provides complete documents.
  • MATLAB is a production-oriented programming language. As a result, the MATLAB code is ready for production. All that is required is the integration of data sources and business systems with corporate systems.
  • It has the ability to convert MATLAB algorithms to C, C++, and CUDA cores.
  • For users, MATLAB is the best simulation platform.
  • It provides the optimum conditions for performing data analysis procedures.


Tableau is a data visualization program that is among the most competent on the market. In data analytics, the approach of data visualization is commonly employed. In only a few minutes, you can use Tableau to produce the best data visualization for a large amount of data. As a result, it aids the data analyst in making quick decisions. It has a large number of online analytical processing cubes, cloud databases, spreadsheets, and other tools. It also provides users with a drag-and-drop interface. As a result, the user must drag and drop the data set sheet into Tableau and set the filters according to their needs.

Some of the highlights of Tableau are:

  • It gives the most compelling end-to-end analytics.
  • It provides us with a system of high-level security.
  • It is compatible with practically all screen resolutions.


Minitab is a data analysis program that includes basic and advanced statistical features. The GUI and written instructions can be used to execute commands, making it accessible to beginners and those wishing to perform more advanced analysis.

Some of the highlights of Minitab are:

  • Minitab can be used to perform various sorts of analysis, such as measurement systems analysis, capability analysis, graphical analysis, hypothesis analysis, regression, non-regression, etcetera.
  • It enables you to create the most effective data visualizations , such as scatterplots, box plots, dot plots, histograms, time series plots, and so on.
  • Minitab also allows you to run a variety of statistical tests, including one-sample Z-tests, one-sample, two-sample t-tests, paired t-tests, and so on.


Microsoft Excel is undoubtedly one of the best and most used statistical tools for beginners looking to do basic data analysis . It provides data analytics specialists with cutting-edge solutions and can be used for both data visualization and simple statistics. Furthermore, it is the most suitable statistical tool for individuals who wish to apply fundamental data analysis approaches to their data.

You can apply various formulas and functions to your data in Excel without prior knowledge of statistics. The learning curve is great, and even freshers can achieve great results quickly since everything is just a click away. This makes Excel a great choice not only for amateurs but beginners as well.

Some of the highlights of MS Excel are:

  • It has the best GUI for data visualization solutions, allowing you to generate various graphs with it.
  • MS Excel has practically every tool needed to undertake any type of data analysis.
  • It enables you to do basic to complicated computations.
  • Excel has a lot of built-in formulas that make it a good choice for performing extensive data jobs.


RapidMiner is a valuable platform for data preparation, machine learning, and the deployment of predictive models. RapidMiner makes it simple to develop a data model from the beginning to the end. It comes with a complete data science suite. Machine learning, deep learning, text mining, and predictive analytics are all possible with it.

Some of the highlights of RapidMiner are:

  • It has outstanding security features.
  • It allows for seamless integration with a variety of third-party applications.
  • RapidMiner’s primary functionality can be extended with the help of plugins.
  • It provides an excellent platform for data processing and visualization of results.
  • It has the ability to track and analyze data in real-time.


Apache Hadoop is an open-source software that is best known for its top-of-the-drawer scaling capabilities. It is capable of resolving the most challenging computational issues and excels at data-intensive activities as well, given its distributed architecture. The primary reason why it outperforms its contenders in terms of computational power and speed is that it does not directly transfer files to the node. It divides enormous files into smaller bits and transmits them to separate nodes with specific instructions using HDFS. More about it here.

So, if you have massive data on your hands and want something that doesn’t slow you down and works in a distributed way, Hadoop is the way to go.

Some of the highlights of Apache Hadoop are:

  • It is cost-effective.
  • Apache Hadoop offers built-in tools that automatically schedule tasks and manage clusters.
  • It can effortlessly integrate with third-party applications and apps.
  • Apache Hadoop is also simple to use for beginners. It includes a framework for managing distributed computing with user intervention.

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There are a variety of software tools available, each of which offers something slightly different to the user – which one you choose will be determined by several things, including your research question, statistical understanding, and coding experience. These factors may indicate that you are on the cutting edge of data analysis, but the quality of the data acquired depends on the study execution, as with any research.

It’s worth noting that even if you have the most powerful statistical software (and the knowledge to utilize it), the results would be meaningless if they weren’t collected properly. Some online statistics tools are an alternative to the above-mentioned statistical tools. However, each of these tools is the finest in its domain. Hence, you really don’t need a second opinion to use any of these tools. But it’s always recommended to get your hands dirty a little and see what works best for your specific use case before choosing it.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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