Machine Learning vs. Statistics: How Do They Differ?
Learn about the relationship between statistics and machine learning, as well as the crucial concept of statistical learning, which bridges the two.
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Key takeaways
Statistics provides one of the key foundations for machine learning.
Many ML algorithms are heavily statistical.
Statistical learning bridges the two. It's a specific approach to machine learning that focuses on understanding why models work.
Explore the relationship between machine learning and statistics, as well as what statistical learning offers ML. Afterward, build your knowledge of machine learning with Stanford Online and DeepLearning.AI's Machine Learning Specialization.
Machine learning vs. statistics
The relationship between statistics and machine learning (ML) is essentially one of foundation and application. Statistics is one of the key fields—alongside computer science, mathematics, and information theory—that machine learning uses in its multi-disciplinary approach.
Statistics is the mathematical science of collecting, analyzing, and interpreting data. It focuses on understanding relationships, testing hypotheses, and drawing inferences about populations from samples.
Machine learning incorporates statistical principles to build predictive models. Many ML algorithms are heavily statistical, relying on models such as linear regression and Bayesian statistics to perform predictions at scale.
The primary difference lies in their objectives. Statistics uses statistical modeling to understand why relationships exist in given datasets, while machine learning applies statistical methods to massive datasets to determine what will happen (aka automated prediction).
Machine learning vs. statistics example
Consider credit scoring as an example: A statistical approach would build a model that shows exactly how people's income, credit history, and debt ratios influence their default risk.
A machine learning approach, on the other hand, might deploy neural networks that achieve high prediction accuracy for default risk, but can't easily explain why specific lending decisions were made.
Machine learning and statistical learning
Statistical learning is a crucial concept in machine learning and statistics, as it effectively bridges the intersection between the two. Statistical learning represents a specific approach to machine learning that focuses on understanding why machine learning models work, when they're reliable, and how confident practitioners can be in their predictions through established statistical theory and rigorous mathematical frameworks. That's because the nature of machine learning often involves accepting complex models that sacrifice interpretability for performance.
For example, in a movie recommendation system, machine learning focuses on whether recommendations lead to user engagement. Statistical learning would additionally examine why the algorithm works, analyzing the statistical significance of user rating patterns, calculating confidence intervals for predictions, and validating the mathematical assumptions underlying the recommendation model.
Machine vs. statistical learning: key differences
| Category | Statistical learning | Machine learning | |
|---|---|---|---|
| Primary uses | Hypothesis testing, inference, understanding relationships | Prediction, classification, pattern recognition, automation | |
| Audience | Statisticians, researchers, data scientists | Engineers, developers, practitioners focused on performance | |
| Core philosophy | "Why does this work?" | "Does this work?" | |
| Data requirements | Smaller, cleaner datasets with clear assumptions | Large datasets; messy or unstructured data | |
| Model interpretability | High - emphasis on understanding coefficients and relationships | Variable - often "black box" for better performance | |
| Mathematical foundation | Strong statistical theory, probability distributions, inference | Algorithm design, optimization, computational methods | |
| Validation approach | Statistical significance, confidence intervals, hypothesis tests | Cross-validation, holdout testing, performance metrics | |
| Typical applications | Clinical trials, economic modeling, research studies | Recommendation systems, image recognition, fraud detection | |
| Key strengths | Rigorous inference, uncertainty quantification, regulatory compliance | Scalability, handling complex patterns, predictive accuracy | |
| Common tools | R, SAS, SPSS, classical statistical packages | Python, TensorFlow, scikit-learn, cloud ML platforms |
When to choose: ML, statistics, and statistical learning
Some projects raise the question of which methodology will be best: statistical hypothesis testing, building a predictive ML model, or applying statistical learning for an interpretable solution. Let's review when you might want to choose each option.
Statistical learning: Choose statistical learning when you need regulatory compliance, scientific validation, or explainable AI.
Machine learning: Choose ML when prediction accuracy and scalability are high priorities
Statistics: Choose traditional statistics for research, hypothesis testing, and situations that require mathematical proof of relationships.
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