Data Mining vs Statistics: 7 Critical Differences

on Data Integration, Data Mining, Data Visualization, Insights, Statistics • May 13th, 2022 • Write for Hevo

Data Mining vs Statistics | Hevo Data

Expertise in making sense of data often defines success in modern business environments. Organizations are on a never-ending quest to acquire more data without even planning what to do with them. Data Mining and Statistics are two universal terms in this domain.

People often use them interchangeably or consider them overlapping, but they are like chalk and cheese in reality. This post talks about Data Mining and Statistics. You will also read about the differences between Data Mining vs Statistics.

Table Of Contents

What is Data Mining?

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Data mining is about looking deep into data to derive hidden patterns. Data in this context can be anything: natural language sentences, images, or numeric data. Data Mining involves using a variety of techniques, including domain understanding and mathematical rules. 

In the earlier days, Data Mining used to be a manual process, but with the advent of cheap processing power, it has become a semi-automatic process. It is usually performed by a Data scientist, business intelligence developer, or business analyst with data exposure.

Numerous tools are available to mine data, including statistical and visualization frameworks. A Data mining professional usually has exposure to tools related to storage, exploration, visualization, and statistics. Even a database with good querying ability is a productive tool for an expert data miner. 

Data Mining can be divided into the below concepts on a high level.

  • Grouping Data According to Patterns: This involves techniques like clustering and classification. Clustering group data without prior knowledge of the number of output groups. Classification attempts to categorize data points to one of the predefined labels.
  • Finding Anomalies: Extracting data that is significantly different from other data points in the set is required to establish patterns. Concepts like Normal distribution and statistical rules are employed to extract anomalies.
  • Deriving Relationships: Extracting cause and effect relationships can be done statistically. Association rule learning is commonly used to accomplish this. 
  • Predictive Modeling: While it may seem like an entirely different concept compared to Data Mining, predictive modeling is often used to uncover insights like reasons for specific customer behavior and estimate other unknown outcomes.  

Verifying results obtained through data mining is usually done using a statistical technique called hypothesis testing. Hypothesis testing helps one establish the validity of results found on smaller data to the larger outside world.

Since Data mining often involves dealing with personal information and deriving patterns, it usually raises questions regarding legality and ethics. 

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What is Statistics?

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Statistics is the science of analysis and interpretation of numeric data. It is considered a part of applied mathematics. Using Statistics generally involves drawing conclusions based on a small amount of data and then extending it to the whole population. Population in a statistical sense is the total data where something is applicable. A sample is a subset of the population where an experiment or observation is conducted. 

Statistics can be divided into two on a high level. Descriptive statistics and Inferential Statistics. Descriptive statistics focuses on summarizing the data in terms of different metrics. These metrics could be aggregation metrics like mean, median, or mode. Or it could be metrics related to variation in data like standard deviation, range, etc. Distribution is another term that is generally used with descriptive statistics. It denotes the shape of the data and forms the basis of defining properties like probability distribution functions. 

Inferential Statistics is the method of using descriptive statistics to form deductions about the sample and then extending it to the whole population. It relies on probability distributions and makes deductions based on it. Hypothesis testing is a critical part of inferential statistics. Hypothesis testing establishes how well the sample represents the population and the degree of validity of extending sample results to population results.

An example of this could be using a simple survey among a small percentage of your customers about a product feature and generalizing the results to the whole set of people who uses the product. 

Data Mining vs Statistics: Key Differences

Now that we understand the basics of what Data Mining and Statistics is, let us explore how these are different from each other.

Data Mining vs Statistics: Deriving Insights and Interpreting Data

As evident from the sections above, Data Mining and Statistics are entirely different concepts. Data Mining is the process of deriving useful insights from data. Statistics is the science of collecting, analyzing, and interpreting data. Statistics can be one of the methods that are used in data mining. 

Data Mining vs Statistics: Quantitative and Generic Input

Statistics is concerned with quantitative data only while Data Mining deals with any kind of data. Deriving numeric metrics out of data is often the first step of using statistics on it.

Data Mining vs Statistics: Exploring Data and Formalizing thoughts

The final result of data mining is often a prediction method, while for statistics, this is more about deducing something based on probability distributions. Data Mining is often exploratory in nature. Statistics is about confirming hypotheses. 

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Data Mining vs Statistics: Importance of Domain Knowledge

Heuristics are thumb rules that are formed based on the knowledge of a domain. Heuristics are very important in data mining and often form the base of exploration. Statistics is about negating all heuristics and interpreting data only on the basis of mathematical evidence and probability. 

Data Mining vs Statistics: Focus on Data Collection

Collecting data and cleaning is an important part of statistics. Data Mining is supposed to work with virtually any kind of data and does not put much emphasis on the collection of data. It is more about working with available data than defining strategies for collecting data/

Data Mining vs Statistics: Tools and Techniques

A Data Mining expert must be aware of tools and techniques used in data storage, exploration, and visualization. This means he must be an expert in a wide range of tools. For storage, it could be anything from a simple relational database to a completely managed flat-file storage like S3. 

Even NoSQL databases are important for a data mining professional. Data exploration tools like SQL and processing frameworks like Spark are also important for Data Mining. Visualization tools like Tableau, PowerBI, etc help him present the results. And Last but not least, Data Miner must also have some background in statistics.

A Statistician works with open source or proprietary tools that help him compute descriptive statistics and derive inferences. This includes open-source tools like R or scikit learn and proprietary tools like SAS, SPSS, minitab, etc. Even a spreadsheet tool like Microsoft Excel or Open Office is a potent tool for statisticians. 


We have now learned about the basics of Data Mining and Statistics. As discussed Data Mining and Statistics are different concepts on their own. While Data Mining is the exploration of data to derive insights, statistics is the science of interpreting data. Statistics is a core part of Data mining, but they are not the same. Data Mining employs statistical techniques to derive prediction models or confirm results, but it is much more than statistics and includes storage, exploration, visualization, etc. 

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