10 Best Statistical Analysis Software Shortlist
Here's my pick of the 10 best software from the 18 tools reviewed.
With so many different statistical analysis solutions out there, finding the right tool can be complicated —especially when it requires searching through dense and often opaque product pages. You know you want to improve data accuracy, efficiently analyze large datasets, enhance patterns and trends detection, and support evidence-based decision-making, but need the right tool. I've got you! In this post, I use first-hand research and my experience consulting for large organizations to bring you the best statistical analysis software on the market.
What is Statistical Analysis Software?
Statistical analysis software refers to computer programs used to perform statistical analyses on complex data. It encompasses a range of tools and techniques for summarizing, interpreting, and visualizing data to uncover patterns, relationships, and trends.
Among the features that make this possible are data import and manipulation, descriptive statistics, inferential statistics, regression analysis, and data visualization tools. Overall, statistical analysis software allows researchers, analysts, and scientists to analyze and interpret diverse data sets, test hypotheses, and derive meaningful insights that help draw reliable conclusions through robust statistical methods.
Overview Of The 10 Best Statistical Analysis Software
Here’s a description of each statistical analysis software that includes the best use cases for each note, any noteworthy features, pros and cons, and pricing information. You’ll also see a screenshot of the interface to give you an idea of what using each tool is like.
IBM SPSS is a statistical software platform with a user-friendly interface that lets users prepare and analyze data without knowing how to code. The tool uses drag-and-drop functionality to perform analysis and provides a range of technical demos to help you get started.
Why I picked IBM SPSS: Some statistical software platforms overwhelm users with a wealth of features. Others limit capabilities to appeal to a novice audience. But IBM SPSS manages to strike a balance between the two, positioning itself as a user-friendly option while also having a range of comprehensive features (like regression analyses). This makes it easy for inexperienced analysts to get to grips with the platform but means they won't have to switch to a different solution once they become competent.
IBM SPSS Standout Features and Integrations
Features that separate IBM SPSS from competitors include a clean, easy-to-use interface and drag-and-drop functionality. All users need to do is point and click on the relevant model to run an analysis on their data. There's a vast number of statistical models to choose from, ranging from basic functions like frequency modeling to advanced regression modeling, and the platform even automates much of the data preparation process.
Integrations include a range of other data analysis tools, including TIMi and JMP, and survey platforms such as Voxco Online and Rotator Survey
Pros and cons
Pros:
- Competitive pricing
- Wide range of statistical models
- Fantastic user interface
Cons:
- Reporting features lack flexibility
- Some very advanced models are missing
Tableau is Salesforce’s visual analytics platform. You can deploy it on your own servers, in the cloud, or natively integrate it with your Salesforce CRM—perfect if you plan to analyze customer data.
Why I picked Tableau: The native integration with Salesforce is a significant selling point for any business that already uses the company’s CRM. It’s pretty much the only statistical analysis software with this kind of integration. Of course, you can connect just about any other data source to the platform, too. The software is also powered by Salesforce’s Einstein AI, which allows you to use the tool to make AI-driven predictions based on your data.
Tableau Standout Features and Integrations
Features include a native Salesforce integration, a standout feature for Salesforce users. The platform also features a drag-and-drop modeling tool (so you don’t need to know how to code to use the platform), Einstein AI, which delivers predictions and recommendations based on your data, and a bespoke data preparation tool, Tableau Prep Builder.
Integrations include Salesforce CRM and a host of other data sources (such as Excel, Amazon Redshift, Snowflake, and Splunk. It also integrates with several other business applications, including AWS, Google Cloud, SAP, Alibaba.com, and Informatica.
Pros and cons
Pros:
- Intuitive dashboard with drag-and-drop functionality
- Huge range of other third-party integrations
- Native Salesforce integration
Cons:
- Can take a while to download large data sets
- Visualization features could be improved
Domo is a cloud-based data platform designed to help everyone in your organization better use business data. The software’s intuitive dashboards and reporting make it simple for even first-time analysts to generate insights and put data to work.
Why I picked Domo: Of all the statistical analysis software tools in this list, Domo is probably the most user-friendly. The software automates data extraction, meaning data can be transferred from spreadsheets to dashboards in real-time. Embedded analytics even lets you share dashboards externally with clients or partners.
Domo Standout Features and Integrations
Features focus on making it easy to understand data. The platform offers personalized and customizable visualizations, custom dashboards, automatic alerts when data changes, and external sharing capabilities.
Integrations include Google Drive, Google Analytics, Microsoft Excel, Dropbox Business, Basecamp, Shopify, Hubspot, and dozens more business apps.
Pros and cons
Pros:
- Great UI
- Real-time data feeds
- Customizable dashboards and reports
Cons:
- Customer support is lacking
- Lacks advanced functionality
SAS Viya is a cloud-based AI, analytics, and data management platform. You can integrate data from almost any source and run collaborative data prep and analysis functions as a team.
Why I picked SAS Viya: Collaboration is at the heart of the SAS Viya platform. Businesses can create a shared space where anyone in the data team can work on analysis together. For example, data engineers can create scalable pipelines, data scientists can transform those data sets into structured data, and business analysts can use advanced analytics tools to derive insights.
SAS Viya Standout Features and Integrations
Features include a wide range of tools that help your team upload and analyze data. Data management capabilities integrate a range of data sources, and a suggestion engine speeds up data preparation. AI tools provide intelligent recommendations that accelerate decision-making. You can even bring your team’s language to the platform.
Integrations are possible with a range of tools using open-source integrations such as the Rest API, Python APIs, Java APIs, and iOS and Android SDKs.
Pros and cons
Pros:
- Very fast processing
- Easy to connect different data sources
- Great collaborative features
Cons:
- Not the most intuitive platform
- Can be expensive depending on usage
Scilab is a free, open-source statistical analysis software under the GPL License. That means you can use the software for any purpose, change the software, and share the software with colleagues.
Why I picked Scilab: Scilab is the only statistical analysis software on this list you can use for free long-term. But that doesn’t mean the software is lacking in capabilities. It still provides many of the data analysis tools and features you need, including numerical analysis, data visualization, and simulations.
Scilab Standout Features and Integrations
Features include pre-built statistical models like interpolation and regression analysis, linear systems of equations, and Fourier analysis. As well as a full range of charts and animations, scientific programming capabilities, and algorithm development.
Integrations There are no native integrations available.
Pros and cons
Pros:
- Fast processing
- Clean UI
- Free to use and edit
Cons:
- Not that intuitive to use
- Lacks features compared to premium products
NCSS is a comprehensive statistical analysis software platform, complete with hundreds of graphic tools, and a unique output navigational system that makes it easier to understand results.
Why I picked NCSS: It can be hard to juggle multiple data visualization outputs with some statistical analysis tools, but not NCSS. The platform’s output window allows each graph to be viewed in a separate window, meaning you can compare analysis side by side within the program.
NCSS Standout Features and Integrations
Features include intuitive data import and management that supports all major file types. This, along with prebuilt procedures available from a dropdown menu, makes it faster for you to analyze data. Over 80 plots are available when it comes time to visualize data, all of which are customizable.
Integrations include most major data platforms and statistical software tools, including Microsoft Excel, JMP, MATLAB, Minitab, SPSS, and Statistica.
Pros and cons
Pros:
- Intuitive platform
- Easy to compare results
- Dozens of visual tools
Cons:
- Hard to manipulate large datasets
- Can be expensive
JMP is a desktop statistical software designed to suit all levels of analysis, from engineers to business analysts to scientists. As you’d expect from any Mac product, it features a clean user interface making it straightforward for anyone to start using the tool.
Why I picked JMP: The native macOS client is a significant selling point, given that not all desktop-based statistical analysis software tools work on the operating system. The platform is not limited because of that, though. You’ll still get all of the tools you need to understand business data delivered through a no-code solution.
JMP Standout Features and Integrations
Features cover everything you’d expect from a statistical analysis tool, including data import and preparation functionality, a full range of templated statistical analysis options, linked analysis and graphics, interactive visualization, and statistical modeling.
Integrations include a range of data sources like Microsoft Access, SQLite, MySQL, and Amazon Aurora, as well as other statistical analysis tools like Minitab, SAP, and SAS Viya.
Pros and cons
Pros:
- Wide range of graphics
- Great dashboarding capabilities
- Native Mac client available
Cons:
- Fixing errors and bugs can be time-consuming
- Can have a long learning curve
Origin Pro is a scientific graphing and data analysis tool used by over half a million engineers and scientists to create visual representations of data without the need for programming.
Why I picked OriginPro: If you want to visually represent data there are few better options than OriginPro. The software has over 100 built-in graph types and allows point-and-click customization of every element of them. You can even save customized graphs as new templates, so you don’t have to create them from scratch for future reports.
OriginPro Standout Features and Integrations
Features include 100+ graph types (such as column, bar, line plot, trellis plot, vector plot, and 3D charts). The software also has a full suite of customization options, annotation features, and a high-speed data-importing function.
Integrations include Microsoft Excel, MATLAB, Microsoft Word, Catman, Touchstone, and Google.
Pros and cons
Pros:
- Good support team
- Intuitive UI
- Great graphical analysis
Cons:
- Lacking help section
- Program occasionally crashes
Minitab is a statistical software platform that provides data visualization, statistical analysis, and predictive analytics functions to help you make data-driven decisions. It provides all of the tools you need to complete Lean Six Sigma projects from start to finish.
Why I picked Minitab: Minitab comes with an integrated DfSS toolkit. You can follow a pre-built DMAIC roadmap or design your own, with the platform's tools linked to each stage of the process. The platform allows you to run Monte Carlo simulations, create a Pugh Matrix, and perform DOE planning. If it’s your first time running an analysis on Minitab, the software offers an Assistant to guide you through the entire analysis and help interpret data.
Minitab Standout Features and Integrations
Features include an integrated DfSS toolkit, a huge range of quality-related tools (run charts, Pareto charts, variables control charts, Tolerance intervals, Acceptance sampling and OC curves, etc.), and simulation and distribution tools. There's also an in-depth education hub, and the company offers on-site or virtual training.
Integrations include several cloud databases, such as Apache HBase and Amazon Redshift, as well as JMP Statistical Software, Qualtrics CoreXM, and Talend Data Integration.
Pros and cons
Pros:
- Assistant tool to help you perform analysis
- Wide range of Six Sigma tools
- Easy-to-use interface
Cons:
- Can’t be used offline
- Can be expensive for some companies
MATLAB is a statistical analysis software for data cleansing, analysis, and visualization. The platform’s live editor lets you interactively perform iterative tasks like cleansing and labeling data.
Why I picked MATLAB: Messy data can ruin results. That’s why MATLAB is suitable for any CFO blessed with a large data set who wants to cleanse and label data before analyzing thoroughly. Don’t let the fact that the tool is primarily used for academic purposes—it’s very useful for analyzing business data and generating meaningful reports.
MATLAB Standout Features and Integrations
Features include the MATLAB Live Editor, which uses a prebuilt family of functions to speed up the data cleansing process. Further, the Data Cleaner app automatically identifies issues with your data set and applies multiple methods to clean data.
Integrations include a range of data platforms and statistical analysis software. These include Microsoft Power BI, Tableau, MongoDB, JMP, EASA, and Azure Cosmos DB.
Pros and cons
Pros:
- Custom visual creation
- Intuitive graphical interface
- Multiple data cleansing tools
Cons:
- Learning curve can be steep
- Requires significant processing power to run
| Tools | Price | |
|---|---|---|
| IBM SPSS | From $99/user/month | Website |
| Tableau | From $840/user/month | Website |
| Domo | Pricing upon request | Website |
| SAS Viya | From $0.55/SU/hour | Website |
| Scilab | Free | Website |
| NCSS | From $2,995 for an annual license | Website |
| JMP | Pricing upon request | Website |
| OriginPro | From $465/year | Website |
| Minitab | From $1780/year | Website |
| MATLAB | Pricing available upon request | Website |
Compare Software Specs Side by Side
Use our comparison chart to review and evaluate software specs side-by-side.
Compare SoftwareOther Statistical Analysis Software Options
Here are a few more worthwhile options that didn’t make my best statistical analysis software list but are still worth checking out.
- XLSTAT
Best for Microsoft Excel users
- Stata
Best for advanced programming
- Looker
Best for Google Cloud users
- KNIME
Best for open-source users
- TIMi
Best for processing large amounts of data
- Phocas
Best for small teams
- GraphPad Prism
Best for scientific research
- SUDAAN
Best for analyzing complex studies
Selection Criteria for Statistical Analysis Software
Wondering how I developed my shortlist? Here’s a short summary of the main selection and evaluation criteria I used to analyze each of the best statistical analysis software for this article:
Core Functionality
The first thing I did was research a wide range of tools to assess the extent to which they met the core functionality you’d expect from a statistical analysis tool. That includes:
- Allowing you to import data from a range of sources
- Making it possible to conduct multiple forms of statistical analysis using the latest techniques
- Visualizing your results in a range of graphs and visualization tools
Key Features
In order to deliver the core functionality, each statistical analysis software had to have most of the following key features:
- Data import functionality: platforms should make it simple to upload datasets or natively integrate with data warehouses.
- Pre-built statistical models: This feature saves time and makes it simple for novice analysts to create in-depth reports and models
- Charts and visualization dashboard: Platforms should have an abundance of charts and graphs to make results easy to understand.
- Documentation and training: Statistical analysis software isn’t always easy to use. Platforms should have in-depth training material to help get you up to speed.
Usability
Statistical analysis can be complicated enough without having to suffer platforms with complicated user interfaces. With that in mind, I prioritized tools that provided an excellent user experience.
Additional points were awarded to platforms that had drag-and-drop functionality, meaning users didn’t have to know how to code to generate insights.
Pricing
I’ve provided several pricing options (subscription, per usage, one-off licenses) so that you can choose a tool that suits your budget and payment preferences.
People Also Ask
Still have questions about finding the best statistical analysis software for your business? My frequently asked questions section below should help.
What are the benefits of statistical analysis software?
One of the biggest benefits of statistical analysis software is how it streamlines the data analysis process. Many of the tools feature machine learning or AI algorithms that automate parts of the process and make it possible for even a beginner to complete a detailed data analysis.
Is there a free statistical analysis software tool?
Scilab is a free and open-source platform you can use for statistical analysis. You can also use a free trial of several of the tools listed above if you only need to use statistical analysis software for a short time.
What are the different types of statistical analysis software?
There are several different types of statistical analysis software, many of which I’ve covered above:
- General tools provide a broad range of features and are ideal for most businesses.
- Open-source statistical analysis software is highly customizable.
- Domain-specific statistical software contains tools that are compatible with your current software, such as Saleforce’s Tableau, and graphical user interface software like JMP.
What types of graphs and visualizations can be produced using statistical analysis software?
The four types of graphs used in statistical analysis are bar, line, pie, and histogram charts. Specifically, statistical analysis software can execute a wide range of models and data visualizations, including regression analysis, run charts, pareto charts, variables control charts.
Can statistical analysis software be customized for specific research needs?
Many statistical analysis software solutions can be customized to handle your specific needs. There are several ways to customize tools, including using your own language, creating custom data visualization reports, integrating software with your data sets, and editing open-source software.
What can you use statistical analysis software for?
Statistical analysis software can be used to perform a number of different statistical analyses:
- Regression analysis
- A/B Testing
- Data visualization
- Customer segmentation
- Predictive analysis
- Data-driven analysis
Did You Find the Statistical Analysis Software You Were Looking For?
I hope you found what you were looking for in my list of the best statistical analysis software for 2024.
These platforms have vastly different use cases and can serve very different purposes, which makes recommending a one-size-fits-all tool impossible. That’s why I’ve tried my best to categorize each of the platforms by their best use case and core features. Hopefully, that helps you find the right one for you.
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