Statistical Learning vs. Machine Learning: What’s the Difference?

Statistical learning and machine learning are closely related fields, but they have different approaches and goals. While you can use either method to analyze and make predictions from your data, statistical learning focuses on understanding the relationships between variables. Machine learning, meanwhile, centers on prediction, classification, and learning. By comprehending how to use each type of learning, you can best understand your data and choose the right technique for your specific needs.

What is statistical learning?

Statistical learning, also referred to as statistical analysis, is a branch of data science focused on understanding and interpreting how variables (or data sets) relate to one another within a data set. This provides the theoretical foundation for many predictive and analytical techniques used in data analysis and machine learning. At its core, the primary goal of statistical learning is to build statistical models that explain underlying patterns in data and make accurate predictions based on those patterns.

You would typically begin your statistical analysis with a hypothesis, or assumption, about your data’s structures. For example, you might hypothesize that someone’s shoe size is closely associated with their height. You can use statistical learning techniques to model this relationship and understand the strength of the association between variables, predict someone’s height based on their shoe size, and determine how other variables influence this relationship (such as gender or age). Essentially, you are taking inputs and modeling outputs. This helps you understand your data at a deeper level and make better decisions based on your information. 

What is statistical analysis used for?

You can use statistical learning in various fields—ranging from public policy to astrophysics to medicine—that require insights, predictions, and pattern recognition within data sets. Ways in which you might use statistical analysis include:

• Testing hypotheses: You can evaluate the assumptions about your data.

• Exploring data: If you’re unsure about what your data might show, you can perform exploratory data analyses to uncover new information and guide further analysis.

• Modeling relationships: You can examine how certain input variables affect output variables and identify factors that influence this relationship. Regression analyses, correlations, and analysis of variance (ANOVA) are popular methods to look at how variables relate.

• Identifying patterns: You can recognize trends and patterns in your data to discover what causes certain changes and predict the movement of certain variables in the future.

Advantages of statistical analysis

Many professionals choose to use statistical analysis, and for good reason. You can choose different types of statistical analysis to handle complex research questions and industry problems, making it modifiable to your data types and needs. Some of the benefits of statistical analysis include:

• Scalability: You can use statistical analysis methods with data sets of all sizes, meaning you can scale your techniques as you collect more data.

• Accuracy: Many statistical methods are robust to common errors, such as model misspecification or unmeasured variables. Choosing models that can handle complex relationships within your data can help you accurately understand your data.

• Versatility: You can use statistical analysis methods that suit continuous, categorical, time-series, and other types of data, depending on your industry. You can also make decisions on your method based on your number of input and output variables, and the data type for each.

• Applicability: Once you learn how to use a specific type of statistical analysis, you can use it in a wide range of applications. For example, you could use the same type of logistic regression analysis to predict whether someone will have a disease based on their medical history or whether a customer will make a purchase based on their online behavior. 

Disadvantages of statistical analysis

Like any method, statistical analyses have several limitations. For one, statistical models often use historical data, meaning they rely on the assumption that patterns will hold fast in the future. This may not always be the case. You need to determine when to challenge these historical trends (such as testing whether recent data shows changes to past trends), when to make predictions based on them, and when to collect new data. 

Many statistical models also rely on strict assumptions, such as independence of variables and normal distributions. Depending on your data, you may have violations of certain assumptions. When this is the case, your statistical analysis setup can become increasingly complicated and may not accurately represent your data.

What is machine learning?

Machine learning is a subfield of artificial intelligence that focuses on creating algorithms capable of learning from data and improving their performance over time, without requiring explicit instructions for every step. While statistical learning primarily aims to understand the relationships between variables and the underlying structure of data, you can use machine learning to uncover hidden patterns in large, complex datasets that might go unnoticed by humans and make predictions on certain outcomes or classifications. In essence, it tackles problems by “thinking” in similar ways to human reasoning. 

Machine learning generally falls into two main categories: supervised and unsupervised. In supervised learning, you provide labeled training data to your algorithm to help it learn from examples. For instance, if you want a model to identify humans in images, you would supply a set of labeled pictures indicating whether each image contains a person. The model then iteratively refines its predictions by comparing them with the labels and adjusting its internal parameters to improve accuracy over time. In unsupervised learning, you provide your model with unlabeled data and leave it to discover patterns, groupings, or structures on its own, without direct guidance.

What is machine learning used for?

You can use machine learning to handle large, unstructured datasets in a variety of ways. Common uses for machine learning include:

• Predictive analytics: When using predictive analytics, you can choose between several types of machine learning algorithms to detect patterns and predict future values. Decision trees, regression models, neural networks, and classifiers are common types you can choose from. 

• Image recognition: Machine learning algorithms can detect objects, identify defects in product images, classify images, and aid in computer vision tasks. 

• Natural language processing (NLP): By enabling machines and humans to communicate directly, you can use NLP algorithms for speech recognition, content generation, automation of tasks, document processing, and data handling. 

• Recommendation systems: Machine learning algorithms can process large amounts of user data to determine the best products and services for individual customers or groups of customers. 

• Autonomous systems: Machine learning enables reinforcement learning, which is when software can make decisions and update its algorithms based on trial and error (such as feedback from its environment). Autonomous systems such as self-driving cars often use this to make decisions on roads, such as obeying road signs and avoiding accidents. 

Advantages of machine learning

The advantages of machine learning, compared to more traditional statistical models, include the way you can handle large amounts of data and scale to different applications. Key advantages of machine learning include:

• Can handle large, unstructured data sets: You can choose many types of machine learning algorithms to handle different types of unlabeled data such as text, images, social media posts, and more. 

• Can scale to new applications and new data: Because machine learning algorithms can learn and adapt to new information, the algorithms you design can scale to different applications and new processes. 

• Can learn from input to output: Machine learning algorithms can learn from labeled data with inputs and outputs without the need for intermediate steps, making it an efficient learning model that can be used for complex tasks and types of data analysis.

Disadvantages of machine learning

While machine learning offers significant advantages, it’s worth considering the disadvantages when deciding whether it’s the right method for you. While machine learning methods can identify patterns within unstructured data, you can’t always see exactly what is happening within the algorithm. This can make it difficult to track down errors or correct misspecifications. If you need to explain how you made decisions, such as in health care applications, this can pose challenges. 

Another potential disadvantage of machine learning is that the accuracy of your model often depends on the quality of your training data. If you have small or skewed data sets, the predictions your model makes, or the way that it learns, can have biases that prevent it from generalizing well to other data.

Statistical learning vs. machine learning: Other things to consider

Statistical learning and machine learning are closely related, but they approach data in different ways. When deciding which approach is right for your data and needs, consider a few areas to help you find the best fit. 

• The complexity of your data: Understructured or high-dimensional data may be better suited for machine learning models. If you have simpler data, traditional statistical models may suit you better.

• Whether you need to explain each decision step: If you need to be able to explain each step in the decision-making process, statistical models may offer more transparency. 

• What your available resources are: Machine learning models often require computational resources and expertise to run effectively. Statistical models may require fewer resources but still require certain assumptions to be met.

Discover more data analysis techniques on Coursera

Statistical learning and machine learning models allow you to analyze your data and gain relevant insights for your industry. To learn more about how to apply different data analytics methods in your field, explore Specializations and Professional Certificates on Coursera. You can start with the Meta Data Analyst Professional Certificate or the Machine Learning Specialization by Stanford and DeepLearning.AI to start learning relevant skills for a career in this area.