Choosing the Right Statistical Software

If you’ve ever taken a statistics or economics class, you’ve probably noticed how many different software programs are out there for data analysis. Whether you’re just learning to summarize data or running complex econometric models, the choice of software can shape your experience. Each program—Excel, Stata, R, Python, SAS, and SPSS—has its own strengths, weaknesses, and ideal use cases.

Let’s start with Excel, the program most people already know. It has significant benefits: it’s easy to use, accessible, and great for small datasets, quick calculations, and visual summaries. Many beginners like Excel because you can see everything in a spreadsheet and don’t have to write code. However, Excel has limits: it’s not built for complex statistical analysis or large datasets, and manual data entry can lead to mistakes. It’s a good tool for exploring data, but not the best choice for in-depth research.

Stata is a favorite among economists and social scientists (and our personal favorite here at Apriori) because it strikes a balance between power and ease of use. Its commands are straightforward, and the documentation is excellent, so you can usually figure things out without much frustration. Most statistical software has a bit of a learning curve, and Stata's is much less steep than others. It has many drop down menus and help pages if you can't remember the exact coding syntax, and it has a data visualization page that you can view and edit, much like excel. Stata really shines in econometrics—things like panel data, difference-in-differences, or instrumental variables. However, it’s expensive and less flexible than open-source tools like R or Python. Still, if you’re doing applied research or policy analysis, Stata is a solid and reliable choice.

Then there’s R, a free, open-source tool with endless possibilities. R is beloved by researchers and data scientists for its huge collection of user-created packages that cover everything from regression models to data visualization. If you want professional-quality graphics or reproducible reports using R Markdown, this is the way to go. The main downside is that R can be intimidating at first—the syntax isn’t always intuitive, and it can take time to get comfortable. But once you do, it’s one of the most powerful tools for statistical analysis and data visualization.

Python is another open-source favorite, and it’s known for being extremely versatile. It’s not just for statistics—you can use it for web development, web scraping, automation, or machine learning. Python’s syntax is readable and logical, which makes it great for beginners, and it has strong libraries for data analysis like pandas, statsmodels, and scikit-learn (plus, like R, Python is free!). However, Python’s statistical tools aren’t as deep as R’s, and creating polished visuals usually takes more effort. Still, if you want a language that bridges data analysis with real-world applications, Python is a great choice.

SAS has been around for decades and remains the go-to software in industries where data reliability and compliance are essential, like healthcare and finance. It handles large datasets well and has excellent technical support. The biggest drawback is its cost—licenses are expensive—and its syntax can feel outdated. SAS isn’t as flexible or popular among new researchers, but it’s still valuable in certain professional environments.

Finally, SPSS is often used in social sciences and education because it’s user-friendly and menu-driven. You don’t have to code much; you can point, click, and get clean tables and charts. That simplicity makes it great for beginners, especially in survey research. The downside is that SPSS is limited when it comes to customization, automation, or more advanced analyses. It’s great for teaching or quick descriptive work, but it won’t take you far in data science or complex modeling.

In the end, no single program is “the best.” Excel is great for learning and quick summaries, Stata for econometrics, R and Python for flexibility and power, SAS for enterprise work, and SPSS for simple survey analysis. Many researchers actually use a combination—maybe cleaning data in Stata, visualizing results in Python, and presenting findings in Excel. We certainly use a mix here at Apriori. The best software depends on what you’re trying to do and how much you want to customize the process.