18 Best Statistical Analysis Software To Let Data Lead You In 2024
Shortlist of the 10 Best Statistical Analysis Software Options
Our one-on-one guidance will help you find the perfect fit.
Finance professionals can use statistical analysis software in many key areas of work, from optimizing budgeting to uncovering patterns or trends that help you improve your investment strategy. You might already use a business intelligence tool for some of this, but statistical analysis software helps you undertake more complex data modelling that can enable hypothesis testing and predictive analytics.
A common example of this would be a CFO running a regression analysis on the company's profitability. They'd use a statistical analysis tool to analyze influencing factors like sales performance or operational costs to see how they impact profitability, which could then inform decisions around budgeting.
Each tool has its pros and cons, so, having deeply researched the use cases, and differences between statistical modelling software, I've put together a shortlist of the best tools, explaining why each one stands out, their features, their key use cases, and the types of businesses they're best suited to.
Price Comparison Chart
As you get started, I want to give you a bit of pricing information on the tools I chose, in case you're working off a set budget.
| Tools | Price | |
|---|---|---|
| IBM SPSS | From $99/user/month | Website |
| Tableau | From $70/user/month (billed annually) | Website |
| Domo | Pricing upon request | Website |
| JMP | Pricing upon request | Website |
| Stata | From $840/year | Website |
| SAS Viya | From $0.55/SU/hour | Website |
| NCSS | From $2,995 for an annual license | Website |
| Scilab | Free | Website |
| XLSTAT | From $345/year | Website |
| OriginPro | From $465/year | Website |
Compare Software Specs Side by Side
Use our comparison chart to review and evaluate software specs side-by-side.
Compare SoftwareOverviews of the 10 Best Statistical Analysis Software
Here, I’ll expand on each of the statistical analysis software solutions that made my top 10 shortlist. I have purposely kept each section brief and followed a uniform structure, to allow you to compare their key features easily.
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
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
Stata is a comprehensive software package that runs the statistical analysis gamut from data preparation and manipulation through to visualization and automated reporting. The program includes Mata, an advanced programming language that adds direct support for matrix programming.
Why I picked Stata: While you don’t need to program to use Stata, the platform’s in-built language gives advanced users a development environment where they can produce and compile code. The tool also offers PyStata, a Python integration that lets you integrate Python into your code.
Stata Standout Features and Integrations
Features include the advanced programming language discussed so far, PyStata, and functionality to incorporate C, C++, and Java plugins in your Stata programs. The programming capabilities of the platform also mean that new community-contributed features are being built and added daily, all of which can be installed with a single click.
Integrations include programming languages like Python, C, C++, and Java.
Pros and cons
Pros:
- Exceptional support
- Regularly updated
- Wide range of support for coding languages
Cons:
- Not as beginner-friendly as other tools
- Steep learning curve
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
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
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
XLSTAT is a leading statistical analysis software solution designed specifically for Microsoft Excel. It natively integrates into the spreadsheet, allowing you to analyze business data without leaving the platform.
Why I picked XLSTAT: If the native integration with Microsoft Excel wasn’t enough, business analysts and CFOs can use XLSTAT without knowing how to code. You can produce in-depth data reports and customizable Excel charts, and replicate many of the other capabilities of the other platforms on this list.
XLSTAT Standout Features and Integrations
Features that make XLSTAT a suitable standalone tool for data analysis include data reporting, custom visualizations, and straightforward data preparation. The tool also comes with a range of add-ons (for marketing, quality control, etc.) that expand functionality even further.
Integrations include Microsoft Excel.
Pros and cons
Pros:
- Easy to use
- Excellent customer support
- Native integration with Excel
Cons:
- Can be difficult for novices to use
- Premium pricing can be expensive
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
Other Statistical Analysis Software Options
Here are a few more worthwhile options that didn’t make my list of the best statistical analysis software list, but are still worth checking out.
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Statistical Analysis Software Selection Criteria
Here's how I determined the tools that would go on this list:
- I researched and compared statistical analysis software, to create an initial list of options.
- I considered common buyer needs and pain points—like ease of use and integration with other tools—to condense the list.
- Finally, I took a pass over the list, using the following evaluation criteria, to come up with the top ten options.
Core Functionality (25% of total weighting score)
To be considered for inclusion in this list, each solution had to fulfill these common use cases:
- Perform descriptive statistics
- Conduct inferential statistics
- Generate data visualizations
- Handle large datasets
- Support multiple data formats
Additional Standout Features (25% of total weighting score)
To help further narrow down the competition, I also looked for unique features, such as:
- Offer predictive analytics
- Include machine learning capabilities
- Support real-time data analysis
- Provide cloud-based collaboration
- Integrate with other software tools
Usability (10% of total weighting score)
To get a sense of the usability of each system, I considered the following:
- Provide an intuitive user interface
- Offer customizable dashboards
- Include drag-and-drop functionality
- Support mobile access
- Offer templates for common tasks
Onboarding (10% of total weighting score)
To evaluate the onboarding experience for each platform, I considered the following:
- Offer interactive product tours
- Provide training videos
- Include pre-built templates
- Support live webinars
- Provide chatbot assistance
Customer Support (10% of total weighting score)
To assess each software provider’s customer support services, I considered the following:
- Offer 24/7 support
- Provide a comprehensive knowledge base
- Support live chat
- Include email support
- Offer community forums
Value For Money (10% of total weighting score)
To evaluate the value for money of each platform, I considered the following:
- Offer a free trial or freemium version
- Provide transparent pricing
- Offer tiered pricing plans
- Provide discounts for annual subscriptions
- Include all core features in base plan
Customer Reviews (10% of total weighting score)
To get a sense of overall customer satisfaction, I considered the following when reading customer reviews:
- Highlight ease of use
- Emphasize customer support experiences
- Mention frequency of updates
- Discuss integration capabilities
- Note overall satisfaction rating
Trends in Statistical Analysis Software
In my research, I sourced countless product updates, press releases, and release logs from different statistical analysis software vendors. Here are some of the emerging trends I’m keeping an eye on:
AI Integration
AI and machine learning are becoming integral features in statistical analysis software. They help users uncover patterns and derive insights from large datasets quickly. For example, IBM SPSS has incorporated AI capabilities to enhance predictive analytics.
Cloud-Based Solutions
More vendors are offering cloud-based statistical analysis tools to provide scalability and remote accessibility. Minitab, for instance, has launched cloud solutions that allow users to perform analyses without the need for local installations.
User-Friendly Interfaces
Emphasis on design and usability is growing. Tools are becoming more intuitive, making advanced statistical techniques accessible to non-experts. JMP's recent updates focus on a streamlined interface that simplifies complex analyses.
Augmented Analytics
These tools use AI to automate data preparation, insight discovery, and sharing. This trend reduces the manual effort required for data analysis. SAS Viya is a good example, which uses augmented analytics to accelerate data-driven decision-making.
Open Source Adoption
There's a notable shift towards open-source software, providing flexibility and community-driven improvements. R and Python are increasingly being integrated into platforms like JASP and jamovi, offering powerful statistical capabilities without licensing costs.
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.
How to Choose Statistical Analysis Software
It’s easy to get bogged down in long feature lists and complex pricing structures. To help you stay focused as you work through your unique software selection process, here’s a checklist of factors to keep in mind:
| Factor | What to Consider |
|---|---|
| Scalability | Will the software grow with your needs? |
| Integrations | Does it integrate with the other tools you use? |
| Customizability | Can you tailor the software to fit your specific requirements? |
| Ease of Use | Is the software intuitive enough for you and your team to use without extensive training? |
| Budget | Does the software fit within your financial constraints? |
| Security Safeguards | Are there robust security measures in place to protect your data? |
| Support | Is there reliable customer support available when you need help? |
| Reporting Features | Does the software offer the reporting capabilities you need? |
Features of Statistical Analysis Software
When selecting statistical analysis software, keep an eye out for the following key features:
- Data visualization: Presents complex data and statistical analysis results in an understandable format through graphs and charts.
- Descriptive statistics: Provides basic statistical measures such as mean, variance, and standard deviation to summarize data sets.
- Hypothesis testing: Allows users to test assumptions about a data set, which is crucial for scientific research.
- Regression analysis: Helps in understanding relationships between variables and predicting future trends based on historical data.
- Data import/export: Facilitates easy import of data from various sources and export of analysis results to different formats.
- Automated reporting: Generates comprehensive reports automatically, saving time and reducing the risk of human error.
- Real-time analysis: Enables analysis of data as it is collected, which is essential for time-sensitive decision-making.
- Data management: Provides tools for organizing, storing, and retrieving data efficiently.
- Advanced statistical methods: Includes a wide range of robust statistical techniques for in-depth analysis.
- Integration capabilities: Allows seamless integration with other software tools, enhancing overall functionality and user productivity.
Benefits of Statistical Analysis Software
Implementing statistical analysis software provides several benefits for your team and your business. Here are a few you can look forward to:
- Increased accuracy: Reduces calculation errors, ensuring more accurate data analysis and conclusions.
- Time savings: Automates complex calculations, allowing for faster data processing and analysis.
- Data visualization: Creates charts and graphs, making it easier to understand and interpret data insights.
- Enhanced decision-making: Provides reliable data analysis, helping teams make informed business decisions.
- Scalability: Handles large datasets efficiently, enabling analysis of extensive information without performance issues.
- Consistency: Standardizes data analysis processes, ensuring consistent results across different datasets and projects.
- Advanced analytics: Offers complex statistical models and procedures, allowing for deeper insights and predictive analytics.
Costs and Pricing of Statistical Analysis Software
Selecting statistical analysis software requires an understanding of the various pricing models and plans available. Costs vary based on features, team size, add-ons, and more. The table below summarizes common plans, their average prices, and typical features included in statistical analysis software solutions:
| Plan Type | Average Price | Common Features |
|---|---|---|
| Free | Nada. Zilch. Zip. Zero. | Basic statistical tools, limited data import options, and community support. |
| Personal | $19-50/user/month | Advanced statistical tools, data visualization, and email support. |
| Business | $51-125/user/month | Collaboration features, integration with other software, and priority support. |
| Enterprise | Custom Pricing | Comprehensive analytics, dedicated account manager, and advanced security features. |
FAQs About Statistical Analysis Software
Still have questions about finding the best statistical analysis software for your business? The 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
Can I use multiple statistical analysis software tools together?
Yes, you can use multiple tools together to leverage their strengths. For instance, you might use R for its extensive statistical libraries and Python for its data manipulation capabilities. Combining tools can offer more flexibility and power in your analyses.
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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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