Statistics vs Machine Learning is always a significant issue that statistics students face. They are still unable to differentiate between machine learning and statistical modeling.
The objective of statistics and machine learning is almost the same. But the significant difference between both is the volume of data and human involvement for building a model.
In this blog, I am going to share with you the difference between statistics vs machine learning. Before we get started, let’s have a look at the definition of machine learning and statistics.
Statistics
Table of Contents
Statistics is all about the study of collection, analysis, interpretation, presentation, and organization of data. Whenever we use statistics in scientific, and industrial problem, we begin the process by deciding a statistical model process.
Statistics plays a crucial role in human activity. It means that with the help of statistics, we can track human activities. It helps us in deciding the per capita income of the country, the employment rate, and much more. In other words, statistics help us to conclude from the data we have collected.
Machine learning
Machine learning is the future technology. It is developing at a rapid pace. During the last few years, machine learning has reached the next level. It is used in various fields like fraud detection, web search results, real-time ads on web pages and mobile devices, image recognition, robotics, and many other areas.
Machine learning is a part of computer science. It has been evolved from the study of computational learning and theory in artificial intelligence. Machine Learning work with AI. In other words, machine learning gives the ability to the computers to learn new things with the help of some programs.
Machine learning is also helpful to make predictions on data. It constructs some algorithms that are operated by a model creation, and it is used to create data-driven predictions. Machine learning has played a crucial role in the functionality of human society.
Difference Between Statistics vs Machine Learning
Nowadays, data is the key to success for the business. But data is constantly changing and evolving at a rapid pace. Therefore the business needs some techniques to convert the raw data into valuable one. For this they take help of machine learning and statistics.
Data is collected in the organization from everyday operations. The companies always need to convert the data into valuable data; otherwise, the data is no more than the garbage.
Industries using statistics
Almost every industry use the statistics. Because without statistics, we can’t get the conclusion from the data. Nowadays, statistics is crucial for various fields like eCommerce, trade, psychology, chemistry, and much more.
Business
Statistics is one of the significant aspects of companies. It is playing a crucial role in the industry. Nowadays, the world is becoming more competitive than ever before.
It is becoming more difficult for the business to stay in the competition. They need to meet the customer’s desires and expectations. It can only happen if the company takes quick and better decisions.
So how can they do so? Statistics play a crucial role in understanding the desires and expectations of the customers. It is, therefore, important that brands take quick decisions so that they can make better decisions. Statistics offer useful insights to make smarter decisions.
Economics
Statistics is the base of Economics. It is playing a crucial role in economics. National income report is essential indicators for economists. There are various statistics methods perform on the data to analyze it.
Statistics is also helpful in defining the relationship between demand and supply. It is also required in almost every aspect of economics.
Mathematics
Statistics is also an integrated part of mathematics. Statistics help in describing measurements in a precise manner.
Mathematicians frequently use statistical methods like probability averages, dispersions, estimation. All these are also an integral part of mathematics.
Banking
Statistics plays an essential part in the banking sector. Banks require statistics for the number of different reasons. The banks work on pure phenomena. Someone deposits their money in the bank.
Then the banker estimates that the depositor will not withdraw their money during a period. They also use statistics to invest the money of the depositor into the funds. It helps the banks to make their profit.
State Management
Statistics is an essential aspect of the development of the country. Statistical data is widely used to take administrative level decisions. Statistics is crucial for the government to perform its duties efficiently.
Industries using machine learning
The evolution of computer and technologies has produced machine learning. Machine learning has changed the way we live our lives. There are lots of industries which are using machine learning.
- Google is using machine learning in their self-driven cars. Netflix is one of the most excellent examples of machine learning technologies. Netflix is using machine learning to personalize the content for its customers.
- It analyzes human behavior and then provides the best-matched content to the customer. Machine learning is also helpful in fraud detection, and it helps the brands to be safe in almost every platform.
- Machine learning is getting more popular because the data is also growing at a rapid pace. It allows us to analyze the massive amount of data in less time and low cost with the help of powerful data analysis methods. It helps us to quickly produce models that can analyze the massive amount of data and deliver faster solutions, even on a large scale.
Business
Brands are using machine learning to create various models to examine their performance. Machine learning allows the brands to create thousands of model in a week.
It is making the brands more effective and better for the long term. Machine learning also offers various data techniques that are quite helpful for the business to meet the needs of brands in almost every sector.
It is making the brands more effective and better for the long term. Machine learning also offers various data techniques that are quite helpful for the business to meet the needs of brands in almost every sector.
Decision Making
Machine learning is also helpful in decision making. It helps to reproduce the known patterns and knowledge.
These patterns automatically applied to the data we have collected from various sources. Thus it helps the concerned people to take better decision and actions.
Neural Networks
Neural networks were used for data mining applications. But after the evolution of machine learning, it is possible to create multiple neural networks that are having many layers.
Statistics vs Machine Learning
They belong to different schools
Machine Learning
Machine learning is a subset of computer science and artificial intelligence. It deal with building a system that can learn from the data instead of learning from the pre-programmed instructions.
Statistical Modelling
Statistics is a subset of mathematics. It deals with finding the relationship between variables to predict the outcome.
They came up in different eras
Statistics is quite older than machine learning. On the other hand, machine learning got into existence a few years ago. Machine learning comes into existence in the 1990s, but it was not getting that much popular.
But after the computing becomes cheaper, then the data scientist moves into the development of machine learning. The number of growing data and complexity of big data has increased the need for machine learning.
The extent of assumptions involved
Statistics modeling is used to work on several assumptions. Here are the few examples of linear regression assumes.
- The linear relation between the independent and dependent variable
- Homoscedasticity
- For every dependent value mean of error at zero.
- Observations of independence.
- normally distribution of error for each value of the dependent variable
On the other Machine Learning algorithms do assume a few of these things. But in general, are spared from most of these assumptions.
We also need not specify the distribution of the dependent or independent variable in a machine learning algorithm.
Also Read
- SPSS vs Excel: Which One is The Best Tool For Statistics
- Probability vs Statistics: Which One Is Important And Why?
Types of data they deal with
Machine learning offers a wide range of tools. For prediction, the data on the fly, we use online learning tools. These tools are the most potent tools and capable of learning from a trillion of observation on one by one basis.
But the prediction and lean can do simultaneously. They make predictions and learn simultaneously. On the other hand, statistical models are generally applied for smaller data with fewer attributes.
It is quite overwhelming to process the massive amount of data using statistics.
Predictive Power and Human Effort
It is a common thing that nature will not assume anything before forcing an event to occur. So the less the assumption in the predictive model, the higher will be the predictive power.
Machine learning is used to reduce human efforts. Machine learning is based on the iteration where the algorithms try to find the pattern in the given data set.
Usually machine not work on the comprehensive data and is assumption independent. But the predictive power is very strong of these models.
On the other hand, the statistical model is based on mathematics intensive and coefficient estimation.
Statistics vs Machine Learning: Uncovering the True Power Behind Data Analysis
| Feature | Statistics | Machine Learning |
| Purpose | Focuses on analyzing, summarizing, and drawing conclusions from data using mathematical models. | Focuses on building models that learn from data to make predictions or decisions. |
| Data Requirement | Typically requires smaller datasets and relies heavily on the quality of data for inference. | Can handle larger datasets and performs well with big, complex, and unstructured data. |
| Modeling Approach | Emphasizes understanding the relationship between variables (e.g., regression models). | Prioritizes the ability to make accurate predictions, sometimes at the cost of interpretability. |
| Techniques | Common techniques include hypothesis testing, regression analysis, and probability distributions. | Common techniques include neural networks, decision trees, support vector machines, and ensemble methods. |
| Assumptions | Usually founded on strong assumptions about the distribution of the underlying data. | Works with fewer assumptions, and models can learn complex patterns directly from the data. |
| Interpretability | Models are generally more interpretable, allowing for clear understanding of the data relationships. | Models, especially deep learning, can be harder to interpret, often considered a “black box.” |
| Focus | Explanation-oriented: Focuses on explaining the relationship between variables. | Prediction-oriented: Focuses on maximizing prediction accuracy. |
| Evolution | Traditional field with roots in mathematics and data analysis for centuries. | Modern field that evolved from AI and computer science, rapidly advancing with technological progress. |
| Use Cases | Mostly used in academic research, scientific studies, and industries that rely on inferential data analysis. | Widely used in areas such as autonomous vehicles, recommendation systems, speech recognition, and image classification. |
| Powerful In | Suitable for small datasets and where explanation of data relationships is key. | More powerful for complex tasks like image recognition, natural language processing, and large-scale predictive tasks. |
Statistics vs Machine Learning: Which is More Powerful
More than ever before, the conflict between statistics and machine learning is pertinent in today’s data-driven society. Because statistics is primarily concerned with discovering patterns in data and drawing conclusions from samples, it is well-suited to situations where precision and understanding are paramount. By doing so, scientists can learn the “why” underlying the numbers.
But machine learning is great at processing massive datasets and automating pattern recognition, so it can make good predictions with little to no human intervention. When confronted with complicated problems, organizations typically find that their objectives dictate the best course of action, whether that’s to gain deeper insights or to harness the predictive power of algorithms.
Ultimately, the most powerful approach may not be a matter of choosing one over the other between Statistics vs Machine Learning but rather integrating the strengths of both to unlock richer insights and drive informed decision-making.
Conclusion
Now you make have get the precise comparison between statistics vs machine learning. One more last thing I would like to mentioned here that machine learning without statistics is nothing.
If you’re a statistics student and want help in statistics then you can get the statistic homework help from us at nominal charges. You can also get the assignments math from our experts at very reasonable prices.
FAQs
What is the key difference between statistics and machine learning?
Statistics focuses on drawing inferences from data using established theories and methods, while machine learning is primarily concerned with building models that can learn from and make predictions based on data without being explicitly programmed. Essentially, statistics often emphasizes understanding the underlying patterns in data, while machine learning emphasizes prediction accuracy.
Is machine learning more powerful than traditional statistics?
It depends on the context. Machine learning can handle large datasets and complex patterns that are difficult for traditional statistical methods to model. However, traditional statistics is still powerful when it comes to hypothesis testing, causal inference, and small sample sizes where interpretability is crucial.
