Top Free and Open-Source Statistical Analysis Software

Know the best open-source statistical analysis software and their features that make them stand out!

Statistical Analysis is a component of data analytics, collection, and interpretation of data to uncover patterns and trends, gather research interpretations, statistical modelling or designing surveys and studies. Statistical Analysis Software is used to solve complex business problems.

Features in Statistical Analysis Software

Statistical software comes with expansive libraries of readymade/ready to use statistical procedures. This might include 100’s of prewritten procedures to deliver or amplify the functionality of the program.

These come with a range of robust statistical methods and a set of tools to accomplish various specialized and enterprise wise needs from analysis of variance and linear regression to Bayesian inference and high-performance model selection for massive data.

Statistical Analysis Software is made to be scalable and to run across multiple platforms. They can access nearly any data source, can easily integrate into any computing environment, scaling too address larger or even more complex analytical problems.

These also come with some regular updates, thus delivering the statistical methods and high-performance computational tools along with user-requested enhancements.

What is the statistical analysis package?

Statistical Analysis Software (SAS) understands the data type; access it from any software and any format and solve complex business problems. It is often used for collection, organization, analysis, interpretation, and presentation of data.

It is a general-purpose statistical package used in academic research for editing, analyzing, and presenting numerical data. It is compatible with various file formats that are commonly used for statistical data such as Excel Spreadsheet Software (EPSS), plain text and relational SQL databases.

What are the tools of statistics?

F-test, T-tests, regression analysis are the most common and convenient statistical tools to quantify the comparisons.

What are the statistical analysis methods?

Statistical Analysis makes use of two main methods:

  • Descriptive Statistics: It is the process of summarizing data from a sample using indexes such as the mean or standard deviation.
  • Inferential Statistics: It is the process of concluding data that are subject to random variation like the variation in sampling or observational errors etc.

What are the uses of statistical packages?

Statistical packages allow the users to decide what information they need to collect, else the data goes waste.

What is the difference between statistical analysis and data analysis?

Statistical analysis can be used to gain an understanding of a larger population by analyzing the information of a sample. Data analysis can be used to inspect, present, report the data in a way that is useful to non-technical people.

What are the 5 basic methods of statistical analysis?

Basic methods of statistical analysis include: mean, standard deviation, regression, hypothesis, testing, and sample size determination.

Here is A list of a top free and open-source statistical analysis software:


It stands for the Journal of Statistical Software and is a free and open-source graphical program for statistical analysis. It comes with a user-friendly interface and offers standard analysis procedures for Integrated with The Open Science Framework (OSF) and support for APA format (copy graphs and tables directly into Word). It can be used both for Frequentist analyses and Bayesian analyses. It updates all results dynamically. It comes with a spreadsheet layout and an intuitive drag and drop interface. It showcases progressive disclosure for increased understanding. It possesses annotated output for communicating your results.


It stands for statistics open for all. It is a user-friendly, open-source statistics analysis and reporting package, to make users export data into an Excel spreadsheet format by using SOFA Statistics. It also enables users to perform easy data recording using a simple form. The data analytics that is output by SOFA Statistics are easily shareable.


GNU PSPP is a free statistics software compatible with SPSS language to perform descriptive statistics, T-tests, anova, linear and logistic regression, measures of association, cluster analysis, reliability, non-parametric tests, and factor analysis. It currently features high-quality output formatting, a user-friendly interface, a command-line interface to allow users to rapidly perform analysis, data preprocessing, analysis, visualization. It can be built in a wide range of platforms.


Scilab is an open-source statistical tool that performs data analysis and modelling. It is better known for Descriptive statistics, Probability distributions, Linear and nonlinear modelling, Machine learning, Regression and Classification. It as well calculates Central tendency, Data with missing values, Descriptive statistics, Measures of dispersion and Measures of shape.


Jamovi is one of the best free statistical software to bridge the gap between the researcher and statistician. It makes the statistics easy, it is based on R statistical language and is open-source and free for the scientific community where it finds its purpose.


It is interactive, free, and open-source statistical software for MAC, Linus and Windows. It comes up with features like descriptive statistics, variables and operations, linear and generalized linear models, time series, matrix algebra, design of experiments, multivariate analysis etc.


It is free statistical software for scientific data analysis with functions for data manipulation, univariate, plotting, ecological analysis, multivariate statistics, time series and spatial analysis, stratigraphy and morphometrics. It might also help users with a spreadsheet-type data entry form; it comes with an interactive user interface and scripting. It comes with kernel density estimation, 3D scatter, percentile, ternary, Graph, bubble, matrix, spindle, survivorship, box, histogram, scatter, surface, and normal probability plots and curve fitting.


It is a free statistical analysis tool used for research in science and R&D (Six Sigma) in a technical environment. It is used for fast analysis, design of experiments, basic statistics, Gauge R&R and Sample size calculations. It comes along with a couple of basic statistics like Anderson Darling normality test, Wilcoxon–Mann–Whitney-test, Compare with, Mean, Correlation test, Sample size calculations, Variation F-test, Difference, Min/Max, STDEV, Regression, Multiple linear regression, Kutosis, Cp Cpk % out of tolerance, Sample counting, t-test, Skewness, Generate distribution, Proportion calculation, Chi-Square test, filter, Multiple linear regression, One-way Anova, Distribution fitting, Graphs, Kruskal-Wallis Test, Nested Gauge R&R (destructive), Gauge, Design of experiments (DOE), Response Surface Methodology (RSM), Reliability Weibull analysis and Weibayes.


It is a powerful, free, and open-source statistical analysis software package that combines complex and statistical tools (within R) with a user interface that is user-friendly and intuitive. It comes with requisite dataset checks to invalidate the results of the analysis are identified before analysis.


It is one of the leading free and open-source statistical software that is designed to solve business and research problems by random analysis, geospatial analysis, hypothesis testing and predictive analytics.

Conclusive: How to choose amongst the best Stats software?

Hope you get some clarity about the reason why statistical tools are used, which tools are currently topping the charts and how do they differ from each other. There is many other free stats software that works on the similar lines besides the above-mentioned software like Atom, Brackets, Bluefish, Visual Studio Code, Notepad++, Cuda text, Emacs, ConText, Editpadlite and Komodo IDE. If you wish to discuss or know something about statistical/analytical tools or want us to add something to this list, reach out to us.