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

Machine learning (ML) and statistics are important in data analysis but serve different purposes. Machine learning focuses on how computers use data to learn, and statistics help interpret data to solve problems. Ultimately, 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 by providing 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 brain's learning 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 typically has three 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—it's based on what movie you pick among its recommendations. As you continue to rate movies the algorithm recommended, its picks will become more attuned to your tastes. 

Applications of machine learning

Many different 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 ML algorithms exist, giving you various options regarding the budget and needs of your application. 

Machine learning algorithms also provide an iterative advantage over human data processing as they learn. This process occurs without human supervision and can 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 AI model requires regular data and algorithmic code updates to remain effective long-term.

• 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 with the necessary math, computer science, and technology background.  

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 also to 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. Finding the regression in a data set creates 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 a data set's highest and lowest outliers to determine its distribution over the entire data set range and its mean. A higher standard deviation indicates a large distribution data set, while a lower 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. 

Applications of statistics

Statistics has applications across society, with industries like health care, education, business, sports, and government relying on its tools. 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 player and team performance data, helping them optimize their abilities in-game.

While many professional industries rely on statistics, they also play a role in daily 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 organized information and evidence. If you have raw data, making a decision can be difficult because patterns may not be immediately visible. Statistics help reveal these patterns.

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 ask a question in a survey. Does asking the question influence the respondent in one way or another? 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 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.

In contrast, 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 faster 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 want to gain skills in traditional statistics, consider the Introduction to Statistics course from Stanford University on Coursera.