Best Notable Difference Between Statistics vs Machine Learning

Several people have this confusion in their minds regarding what is the difference between statistics and machine learning. Is there something like statistics vs machine learning? Actually, the answer to this question is simple, which we will discuss in this blog.

Statistics is about the individual, hypothesis, population, etc. Two distinct critters, correct? Machine learning is all about foresight, unsupervised learning, managed learning, etc. Well, let us see if they are actually that different or have the same purpose.

According to what Larry Wasserman said, they are both concerned with the related topic: how we are going to learn from data? In this blog, you will find the answer to all your questions. But before we jump straight onto the difference between statistics vs machine learning, let us discuss what exactly these terms are.

What Is Statistics?

Statistics is the branch of science that studies and develops techniques for gathering, evaluating, interpreting, and delivering scientific proof. Statistics is a multidisciplinary research field; statistics analysis has applications in almost all scientific areas, and research problems in different scientific fields drive the advancement of new statistical techniques and theory. Statisticians use a combination of statistical and analytical techniques to develop strategies and research the theory that characterises them.

Applications Of Statistics

There are few examples to illustrate the role of statistics in real life.

  • Medical Study
  • Weather Forecasts
  • Quality Testing
  • Stock Market
  • Consumer Goods

What Is Machine Learning?

Machine learning(ML) is an artificial intelligence (AI) technology that allows machines to learn and develop without having to be directly programmed. ML is the study of how to create computer programmes that can access data and learn for itself.

Learning starts with insights or data, such as experiences, direct feedback, or training, so that we can search for correlations in data and make informed decisions in the future based on the explanations we have. The main goal is for computers to learn on their own, without the need for human interference, and change their behaviour accordingly.

Applications of Machine Learning

Machine learning(ML) is a term in today’s technology, and it’s gaining traction at a breakneck pace. Even if we aren’t aware of it, we use ML in our everyday lives through Google Assistant, Google Maps,  Alexa, and other similar services. The following are some of the most common real-world Machine Learning applications:

  1. Speech Recognition
  2. Image Recognition
  3. Self-driving cars
  4. Medical Diagnosis
  5. Traffic prediction
  6. Product recommendations
  7. Online Fraud Detection
  8. Email Spam and Malware Filtering
  9. Virtual Personal Assistant
  10. Stock Market trading

Short statistics vs Machine Learning Comparison Table

Machine learningStatistics 
Definition Machine learning provides the computer with the capacity to understand with being programmed explicitly.Statistics is described as the study of selection, study, discussion, performance and design of data.
RoleMachine learning examines the construction and study of algorithms that can help individuals to understand and make predictions on data.
Statistics when utilized to a query, then the method usually starts with choosing a statistical model process or a statistical population.
BenefitMachine learning helps us to instantly give models that can explain data that are more significant and give a faster definition.Statistics has extensive applications in nearly all kinds of area 
UsageThe relationship between the data pointsTo forecast future events or match existing material.
TypesForecasting constant variables, Regression, classificationSupervised learning and unsupervised learning
ApplicationsDescriptive statistics, finding patterns, outliers in the dataThe weather prediction, Predictive modeling, topic modeling 
FieldArtificial intelligence, data science research labsData analytics, Artificial intelligence
Input-outputDatapointsFeatures and labels

Statistical Methods in a Machine Learning Projects

As you have seen in the above sections, both statistics and machine learning are related. Now we are going to see some of the examples of where statistical methods are used in an applied machine learning project.

This will explain that a working understanding of statistics is necessary for successfully working in a predictive modeling problem.

  1. Data Understanding
  2. Model Evaluation
  3. Problem Framing
  4. Data Selection
  5. Data Cleaning
  6. Model Selection
  7. Data Preparation
  8. Model Predictions
  9. Model Configuration
  10. Model Presentation

Scope of machine learning and Statistics


In this modern era, statistics is almost necessary in terms of planning. The authorities of many nations around the world are strictly researching to fetch the economic crisis and problems. Statistical techniques measured by statistical analysis are extremely helpful in solving statistics problems. The basic terms of mathematical formation are combined with a large variety of disciplines. 

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Here are a few examples of using statistical information, i.e., business, industry, computer science, government, health science, and other terms of regulations. The same skill candidates can also apply for Indian analytical services and economics services exams.

Machine learning: Machine learning is an invention that helps to improve the services provided by systems, the web, and smartphones. The terms machine learning and artificial intelligence are both interconnected. They are quite distinct in the area of computing.

Machine learning is the department of education that applies computer science and statistics principles to create statistical analysis and models and compare patterns in data. It’s a sort of artificial intelligence that ensures software applications become more right in predicting results without an accurate program. While data mining discovered previously unknown patterns and knowledge, machine learning is used to reproduce known patterns and experiences.

Scope of machine learning in the banking and financial department

AI innovation is utilized in most banking and financial industries because the best possible ramifications of change can give an exceptional outcome, and vast improvement can be found regarding supplanting heritage framework and created ventures. The AI innovation helped the banking and Finance part take the organization’s dynamic, improving client experience and expanding backhand and front-hand staff effectiveness. If machine learning is attentive to predict the future. Then artificial intelligence hardly focuses on programming computers to generate decisions. Through some factors, one can judge the difference between both terms, “statistics vs machine learning.”


It may look like both statistics vs machine learning modeling are two different predictive modeling categories. The difference between both terms has decreased significantly over the past few years. Both statistics and machine learning have received a lot and will continue to come closer in the upcoming years. Understanding the connection and knowing the difference will help statisticians and machine learners to develop their understanding and even use outside expertise.

In this article, we have discussed major differences in both machine learning and statistics and where these two can be implemented. Both machine learning and statistics do contribute to Data Science but they have distinct purposes and make several contributions. In case if you need more information on both the terms you can take Statistics assignment help uk and Machine Learning programming assignment.

Frequently Asked Questions

Does machine learning use statistics?

Statistics is usually called a requirement in the field of applied machine learning. We require statistics to help change observations into data and to clarify questions about samples of observations.

Will machine learning replace statistics?

This is caused in part because Machine Learning has used many Statistics methods but was never intended to replace statistics or even have a statistical basis.

What is a statistical model in machine learning?

A Statistical Model uses statistics to represent the data and then conduct analysis to understand any connections between variables or find insights. Machine Learning uses mathematical and or statistical models to get a general knowledge of the data to make forecasts.

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