Statistical Analysis
What it is and why it matters
Statistical analysis is the science of collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. While most decisions are made with some uncertainty, statistical analysis turns raw data into insights you can trust for decision-making. With statistical analysis, you can separate meaningful patterns from noise, reliably test assumptions and draw defensible conclusions from data.
History of statistical analysis
Statistical analysis evolved in response to very real, practical problems. Long before modern data science or big data, people needed reliable ways to measure uncertainty, compare outcomes and turn raw data into insights for making better decisions.
Statistical thinking traces back to ancient Egypt and Mesopotamia, in the data collection used for census recordkeeping, crop records and taxation. But modern statistical analysis took shape in the 17th and 18th centuries, driven by the need to understand risk. Mathematicians Blaise Pascal and Pierre de Fermat’s discussions about how to divide the stakes when a dice game was cut short and how to calculate the chances of throwing a certain side on the dice helped them discover the theory of probability. This theory influenced all future developments in statistics, economics and computing.
In the 19th century, Carl Friedrich Gauss introduced the concepts of normal distribution (the bell curve) and the method of least squares, crucial for error reduction and regression. And in the early 20th century, statistical analysis pioneers formalized key techniques used today, including hypothesis testing, sampling, significance levels, correlation and regression.
As computing power expanded in the late 20th century, statistical analysis that once required weeks of manual calculation could be applied to massive data sets in seconds. This opened the door to statistical applications in fields like finance, health care, operations research and data science. Today, innovative techniques and technologies have changed the capabilities of statistical analysis, but the objective remains the same: Use data responsibly to reduce uncertainty and support better decisions.
A safety net for hospital patients
Hospital patient data does not easily show if a patient is getting better or worse. To capture risk in a new way, the Rothman brothers created a model, then used statistical analysis to refine their ideas and develop the patient acuity score known as the Rothman Index.
Statistical analysis in today’s world
Statistical analysis is the foundation of modern analytics. With statistical tools working alongside AI, your organization can scale rigor, democratize data analysis across the organization and quickly make decisions you can trust.
4 reasons to use PROC PLM for linear regression models
Modern statistical work doesn’t end when a model runs – it requires transparency, repeatability and the ability to validate results over time. PROC PLM in SAS® helps analysts store, manage and reuse regression models reliably, making rigorous, production-ready statistical analysis practical in today’s fast-moving, data-driven environments.
How to visualize regression models in SAS
Visualizing model effects helps analysts move beyond coefficients to insight that stakeholders can interpret and act on. This post shows how the EFFECTPLOT statement in SAS makes it easier to explore and communicate complex regression relationships – an essential capability in modern workflows where clarity and interpretability matter just as much as statistical rigor.
Keep the science in data science
Some assume AI-driven decisions are superior to human judgment. In a podcast, Patrick Hall says the issue with that assumption relates to a classical statistical principle: If underlying assumptions or data inputs are flawed, resulting model outputs are also likely to be flawed. See why effective analytics depends on rigorous statistical thinking, hypothesis testing, bias detection, assumption validation and an awareness of data limits.
Turning statistical insights into treatments
Using advanced statistical modeling with SAS, the Drugs for Neglected Diseases initiative (DNDi) strengthened data transparency and accelerated research on neglected diseases. Maintaining data credibility and compliance with global regulatory standards supports the organization’s mission to deliver affordable, life-changing treatments to underserved patient populations worldwide.
Statistics is all about trying to solve problems. Where to put your mousetrap when you are trying to catch a mouse. ... Statistics gets involved in every aspect of data analytics for dozens of different industries. Jim Goodnight CEO SAS
Models of progress
From modern computing to the rise of AI, this documentary traces how SAS helped transform raw data into real-world decisions by powering breakthroughs in health care, business and global security. Across decades of innovation, discover the story of a company working behind the scenes to solve some of the world’s most complex challenges – quietly influencing industries and affecting millions of lives.
Industries using statistical analysis
Advances in computing power, data availability and modern statistical methods have expanded what’s possible with statistical analysis. Organizations can now work with larger data sets, model complex relationships and evaluate uncertainty with far greater precision – helping industries of every kind turn evidence into confident decisions.
The value of statistics is realized when it is translated, taught and delivered with accessible computing environments to the scientists and analysts who use it. John Sall JMP Founder and Chair; SAS Co-Founder and EVP
Statistical analysis training and education
Many statisticians became interested in the field because of early passions, like collecting Pokémon cards or keeping track of sports stats. As adults, those passions can carry over into the workforce as a love of analysis and reasoning, where their passions are applied to everything from the influence of friends on purchase decisions to the study of endangered species around the world.
Check out our training and education for real-world statisticians:
- Browse our catalog of classes.
- Get free, cloud-based access to SAS software for statistical analysis, data mining and forecasting (for academics).
- Read perspectives about statistics and advanced analytics from expert SAS data scientists on the SAS Data Science blog.
- Join our statistical procedures community, where you can ask questions and share your experiences with SAS statistical products. systems.
How statistical analysis works
Statistical analysis techniques come to life today in statistical programming environments that enable analysts to develop, validate and scale models reliably from hands-on exploration to enterprise deployment.
The process begins with defining a clear question and ensuring the data is appropriate to answer it. Analysts prepare and explore the data, using descriptive statistics to summarize key characteristics such as averages, variance and distribution patterns. From there, inferential methods – like hypothesis testing, regression modeling, estimation and confidence intervals – help determine whether observed results reflect meaningful relationships or normal variation. Throughout the process, statisticians evaluate uncertainty, validate assumptions and ensure conclusions are supported by evidence.
In practice, these techniques are implemented through statistical programming environments where analysts can explore data, test models, and validate results with precision and repeatability. Tools like SAS Data and AI Studio provide a coding-forward workspace for hands-on statistical analysis. SAS® Viya® extends those same methods to larger data, collaborative teams and scalable, production-ready workflows. Together, they enable statisticians to apply core analytical approaches consistently – from exploratory work through operational decision-making.
Common statistical analysis techniques supported in these environments include:
- Statistical programming. From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming provides the foundation for rigorous, data-based decisions across disciplines.
- Econometrics. Modeling, forecasting and simulating economic and business processes can inform planning and strategy. Econometric methods combine statistical theory with real-world data to estimate relationships and anticipate future trends.
- Operations research. Identifying the best course of action among many possible outcomes requires optimization, simulation, scheduling and decision modeling, which helps organizations allocate resources and improve performance.
- Matrix programming. An approach of implementing custom statistical methods and exploratory data analysis using efficient matrix operations gives advanced users the flexibility to design, test and extend their own algorithms.
- Statistical quality improvement. Applying statistical techniques to monitor, control and improve process performance helps ensure quality, safety and consistency across production and operational systems.
Statistical analysis on SAS Viya
Statistical analysis has always required rigor, repeatability and control. SAS Viya brings those same trusted foundations – SAS/STAT® procedures, matrix programming and coding-first workflows – into a modern, scalable environment where teams can analyze larger data, collaborate more easily and operationalize models with confidence. It’s where proven statistical methods meet today’s demands for speed, transparency and enterprisewide decision-making.
