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

Written by Coursera Staff • Updated on

Machine learning uses statistics in its models. Learn about how machine learning differs from statistics and how to approach each discipline.

[Featured Image] A college student is on his computer learning about the difference between machine learning and statistics.

Machine learning (ML) and statistics are both important in data analysis; however, they serve different purposes. Machine learning focuses on how computers use data to learn, and statistics help interpret data to solve problems. Ultimately, both ML and statistics complement each other in problem-solving and making predictions. Many machine learning problems rely heavily on statistical methods, so ML experts need to know when to apply statistical techniques or seek assistance from statistics professionals when an ML model encounters issues.

Explore the differences between machine learning and statistics through an overview of each discipline, its applications, advantages, and challenges.

What is machine learning?

Machine learning is a subset of computer science and artificial intelligence (AI) that tries to mimic the learning of the brain by using algorithms to identify patterns in data sets and make predictions based on those patterns. An ML algorithm bases its predictions on statistical learning by processing more and more data; over time, its predictions become more accurate. A basic machine learning model has three typical steps:

  1. Decision process: Takes in the data to make a guess and search for a pattern the algorithm can optimize

  2. Error function (loss function): Evaluates the model based on the actual outcome and predicted outcome

  3. Optimization process: Examines the error function, then tweaks the decision process in the algorithm to get the predicted outcome closer to the actual outcome

For example, in a movie recommendation algorithm, the “actual outcome” is never finite; rather, it is based on what movie you pick among its recommendations. As you continue to rate movies that the algorithm recommended to you, its picks will become more attuned to your tastes. 

Read more: 10 Machine Learning Algorithms to Know

Applications of machine learning

Many different kinds of industries apply machine learning technology to optimize aspects of their work. Machine learning algorithms surround you in your everyday life as well. Some common applications of machines include:

  • Computer vision: Computer vision allows computers to see patterns in images and videos, and then label and recognize certain aspects of a photo. This enables you to search a keyword like “cat” in a photo database and get images or videos that it determines have a cat in them. This technology also aids self-driving cars. 

  • Speech recognition: Voice-to-text software uses natural language processing (NLP) to convert speech into written text, making smartphone texting more accessible.

  • Medical diagnostics: ML algorithms can process medical records to find patterns in symptoms in patient records to improve diagnoses and even help identify cancerous cells in samples. 

  • Fraud detection: Banks use ML to spot anomalies in financial transactions, which fraud analysts further investigate to uncover fraudulent activity. 

  • Recommendations: Social media apps and streaming services are two examples of recommendation algorithms that use your search, interaction, and rating of specific kinds of content to recommend products or posts more effectively. 

Advantages of machine learning

The advantages of machine learning are vast and include ways for businesses to find patterns in massive volumes of data much faster than traditional statistical methods. These advantages come from the optimization process in a machine learning algorithm that makes its predictions more accurate as time goes on. Another advantage is that many different kinds of ML algorithms exist, giving you various options when it comes to the budget and needs of your application. 

Machine learning algorithms also provide an iterative advantage over human data processing as they learn. This process not only occurs without human supervision but can also uncover a pattern or detail in a data set the algorithm was not initially designed to find, giving you a significant advantage. 

Challenges in machine learning

With all the possibilities machine learning opens up, challenges such as the massive amount of data required to produce an effective ML algorithm remain. Some further challenges include the following:

  • Using poor data quality to train an ML algorithm leads to bad predictions.

  • The underfitting or overfitting of a model leads to inaccurate predictions.

  • An effective model long-term requires maintenance of the data and algorithm’s code.

  • Bias in the weight of items in a data set leads to a biased and ineffective model.

These are just a few challenges in a rapidly evolving industry with a skill gap in the number of ML engineers who have the necessary background in math, computer science, and technology.  

What is statistics?

Statistics is the science of collecting, interpreting, and analyzing data. It is a key component of any functioning machine learning algorithm. Statistics hinges on the use of probabilities to not only understand outcomes in a data set but to also learn something about future outcomes in a population. Since statistics is the study of data sets, its concepts are important in data science:

  • Regression measures two or more variables, with one being independent of the other variables. By finding the regression in a data set, you create a formula to predict future outcomes. 

  • The mean of a data set is the average calculation detailing the frequency of a data point in the set. For example, you can calculate the average grade on test scores to determine a general score.

  • Standard deviation uses the highest and lowest outliers of a data set to determine its distribution over the entire range of the data set and its mean. A higher standard deviation indicates a data set with a large distribution, while a low standard deviation indicates a tighter, more clustered distribution. 

  • Confidence level determines the likelihood that a mean obtained from a sample population occurs across the entire population. 

Read more: 7 Statistical Analysis Methods Beginners Should Know

Applications of statistics

Applications of statistics exist everywhere in society, with many industries, such as health care, education, business, sports, and government using the tools that statistics offer. Some industry applications of statistics include:

  • Governments use statistics to find economic trends, track population demographics, and measure the effectiveness of policies.

  • Health care uses statistics to test drug efficacy based on population samples and collect public health data to monitor community health.

  • Professional sports teams use statistics to collect data on player and team performance, helping them optimize their abilities in-game.

While many professional industries use statistics, they also show up in your everyday life. For example, weather forecasting uses statistical methods to predict future weather patterns. Social media platforms also leverage statistics to show you relevant ads and products. 

Advantages of statistics

The advantage of statistics is its ability to make sense of data sets by providing information and insights into whatever aspect of a population you need to measure. Statistics helps you make informed decisions by providing you with organized information and evidence. If you just have raw data in front of you, making a decision with it is difficult because no pattern is visible; statistics makes this pattern clearer. 

Challenges in statistics

Statistics only happen when a population data sample is available. Therefore, some of the challenges in statistics involve who collects the data, how they collect it, and what they want to measure. Explore these challenges deeper:

  • Who is asking: Point of view and bias are important concepts when collecting data and producing statistics, so it’s necessary to know who the statistics are coming from and what bias they carry. 

  • How they are asking: Statistical researchers must study how they will ask a question in a survey. Does asking the question one way or another influence the respondent? Examining the wording and intent of questions is a challenge researchers face. 

  • Who is being asked: Since statistics work on samples of a population, they have to focus on specific groups, not everyone (in the US, for example). Also, if you are surveying how many times a week humans cry, respondents might lie because they are embarrassed, might not properly recall, or may even want to skew results. 

Machine learning vs. statistics: Other things to consider

When it comes to choosing when to use machine learning versus statistics, it’s important to consider that while machine learning is built upon statistics, the field of statistics extends beyond machine learning and data science. Another aspect to consider is how each discipline creates a model to study. In traditional statistics, the statistician or researcher creates the model to study the data set, whereas an ML engineer creates an initial algorithm that the ML model optimizes as it learns the data set. Due to this automation, ML models can process much larger and more complex data sets more quickly than traditional statistical methods.

Getting started in machine learning and statistics on Coursera

When it comes to machine learning versus statistics, the most important aspect is having quality data, while the rest comes down to which approach best solves your problem. If you’re looking to build in-demand skills in machine learning, explore the Machine Learning Specialization from Stanford University on Coursera. If you’re looking to gain skills in traditional statistics, consider the Introduction to Statistics course, also from Stanford University, on Coursera.

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Introduction to Statistics

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By ...

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