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Statistics is important in data science because it connects questions to evidence and makes uncertainty visible. It helps practitioners judge whether data are suitable, find patterns, estimate how well results may hold beyond the data at hand, and distinguish prediction from causal evidence. It is foundational, but it is not all of data science: programming, domain expertise, data management, computing infrastructure, and model operations matter too.
Why is statistics important in data science?
Data are observations produced by imperfect processes. Measurements can be noisy, samples can omit important groups, and patterns can vary from one dataset to another. Statistical reasoning helps analysts account for those sources of variation rather than treating a result from one dataset as certain or universally true.
The American Statistical Association (ASA) describes statistics as central to data science and artificial intelligence, particularly machine learning and deep learning. In its August 4, 2023 board statement, the ASA writes: “Statistics plays a central role in data science and AI, especially in the areas of ML and deep learning.” The statement also notes that computational and statistical methods increasingly overlap, while statistical contributions such as uncertainty quantification, sampling design, and causal inference remain important. Read the ASA statement.
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How statistics connects a question to the data
A sound analysis starts before model selection. The analyst needs to define what they want to learn and examine how the data were generated. A large dataset is not automatically representative, and precise calculations cannot correct every bias in sampling or measurement.
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- Specify the question. Decide whether the aim is to describe observed data, estimate something about a wider population, predict an outcome, explain a relationship, or support a decision.
- Examine the evidence source. Identify whether the records come from a random sample, an experiment, observational data, or another process. Consider who or what may be missing and how variables were measured.
- Explore the data. Summaries and visualizations can reveal distributions, relationships, unusual values, and data-quality problems. These observations help shape the analysis; they do not, by themselves, establish a general conclusion.
- Choose an appropriate method. The method should fit the question, data-generation process, and assumptions—not simply be the most familiar or complex technique.
- Report the limits as well as the result. Explain what the evidence supports, how uncertainty affects the finding, and what the analysis cannot establish.
The ASA’s curriculum guidance emphasizes statistical thinking, sampling, variability, modeling, data management, and the distinction between prediction and inference. Its Two-Year College Data Science Summit Final Report treats these as connected parts of data science education, rather than as isolated formulas.
Description, inference, prediction, and causation are different
These goals can use overlapping tools, but they answer different questions. Being explicit about the goal prevents a model result from being interpreted more broadly than its design allows.
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- This guide is a perfect overview for the topics covered in introductory statistics courses.
| Goal | What it answers | What it does not establish by itself |
|---|---|---|
| Description | What was observed in the data? | That the same pattern applies to a broader population or future cases. |
| Inference | What can the observed data tell us about a broader population or process, given the sampling and analysis? | That the estimate is free of bias or that an association is causal. |
| Prediction | How well can a model forecast or classify cases it has not used to fit its parameters? | That changing a predictor will cause the outcome to change. |
| Causal explanation | What effect would an intervention have under a specified design and assumptions? | A conclusion beyond the population, setting, or assumptions supported by that design. |
A predictive model may identify a useful association without showing that intervening on one variable changes another. Causal claims require a design and assumptions suited to identifying the effect of an intervention. The ASA highlights the need to distinguish causation from correlation; NIST’s Research Data Framework, Revision 2 provides terminology for research data analysis and machine learning.
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A result is more useful when readers can judge how sensitive it is to the data and method used to produce it. Statistical practice supports that judgment through estimation, uncertainty quantification, validation, and reproducible methods.
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- Estimation and uncertainty: An estimate should be interpreted in light of relevant variability, including sampling, measurement, and model uncertainty. Statistical analysis does not remove uncertainty; it provides ways to model and communicate it.
- Validation: Held-out evaluation or cross-validation can help estimate predictive performance on data not used for fitting. The procedure must suit the data and the intended use; a validation score is not a guarantee of performance in every future setting.
- Reproducibility: Clear methods and a documented workflow make it easier to examine how a finding was produced and whether it can be checked or repeated.
The ASA curriculum report includes modeling and cross-validation among relevant data science outcomes. Together, these practices help distinguish a pattern that fits one dataset from evidence that is likely to be useful beyond it.
Statistics works alongside the rest of data science
Statistics cannot substitute for software that collects and processes data, subject knowledge that gives variables meaning, or infrastructure that makes analysis reliable at scale. The NIST glossary describes data science as combining domain expertise, programming, mathematics, and statistics; the glossary attributes its definition to NIST SP 800-218A. See NIST’s data science glossary entry.
The ASA likewise stresses multidisciplinary collaboration. In practice, statistical methods need to fit into workflows involving data organization, distributed computation, and model lifecycle management. A statistically sound result can still be hard to use if the underlying data pipeline is unreliable or the model cannot be maintained.
What statistical methods are used?
There is no single method that is best for every data science problem. NIST’s Statistical Engineering Division describes work spanning experimental design, statistical modeling, data analysis, Bayesian inference, time series, reliability, calibration, and uncertainty estimation. The appropriate choice depends on the question and evidence: experiments may call for design and analysis that account for assignment and measurement, while time-dependent records may call for methods that represent temporal structure.
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NIST reports that Statistical Engineering Division staff collaborate with more than 90% of NIST scientific divisions. That figure describes collaboration within NIST, not the proportion of data science work generally that involves statisticians. See what NIST’s Statistical Engineering Division does.
A practical way to compare data science approaches
When choosing between methods or reviewing a result, compare the approaches against the same questions. A method that performs well on one dimension may not satisfy another—for example, strong predictive performance does not alone support a causal claim.
- Goal: Is the work descriptive, inferential, predictive, or intended to support a decision?
- Data generation: Do the data come from a random sample, an experiment, observational records, or another process? What coverage limits or biases might follow?
- Uncertainty: Does the analysis address relevant sampling, measurement, or model uncertainty, and communicate it clearly?
- Validation: Is evaluation appropriate to the intended use, based on held-out data or cross-validation where suitable, and reproducible?
- Causal support: Do the design and assumptions justify claims about an intervention, or only an association?
- Operational fit: Can the approach be maintained alongside the required data management, computing, and model monitoring?
These questions reflect the ASA’s emphasis on uncertainty, causation, reproducibility, and the full data workflow, as well as its curriculum guidance on sampling, variability, modeling, and validation.
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