Decode the Role of Statistics in Machine Learning!

How Statistics is used in Machine Learning?

Quick Summary: This blog post delves into the vital role of statistics in machine learning. It highlights how statistical concepts and methods are useful for data analysis, modeling, and decision-making in Machine learning.


Machine learning, a subfield of artificial intelligence, has revolutionized the industries and continues shaping how we interact with technology. Furthermore, machine learning relies heavily on data analysis and mathematical techniques, with statistics playing a significant role in every process step.

Businesses leverage AI and big data on Instagram for targeted advertising, content optimization, audience analysis, and trend prediction, enhancing their marketing strategies and user engagement.

This blog post will explore how statistics is used in machine learning, from data preprocessing to model evaluation, and why it is indispensable for building robust and accurate machine learning models.

What are statistics?

The statistic is one of the steps where we get some meaningful full information from raw ( junks ) data, performing some math or statistical analysis.


Statistics is a branch of science that involves the collection, analysis, and data in large quantities So that you can come up with solving various use cases and conclusions and extract some meaningful full information that helps you in your prediction through ML modals. Whatever the type of the statistical data which is Descriptive or Inferential analyze it with the Test statistics calculator. This makes the data acquisition more realistic and reliable.

In statistics data is divided into two parts

  • Descriptive
  • Inferential

What is Descriptive?

Descriptive statistics comprehension the characteristics of a data set. Descriptive statistics hold two basic categories of measures: measures of central tendency and measures of variability. Measures of central tendency describe the central location of a data set.


An inferential statistic uses data from samples to generalize about a population. Furthermore, It takes statistics from the sample data and uses it to evaluate a population parameter (for example, the population mean).


Generally, population refers to the people who live in a particular area for a specific time. But in statistics, population refers to data on your study of interest. It can be a group of individuals, objects, events, etc. You use populations to conclude.

For example, in the exit poll, it is not possible to gather all given votes before the election ends; the exit poll predicts this through the group of people. The same applies to sampling as well. Additionally, hire ML engineers to collaborate on applying statistical techniques for data analysis and modeling, enhancing decision-making through their expertise.

Sampling Techniques

The sampling data helps predict favors for all populations when we can’t get population data, so we get data from different fields’ opinions as data. Below, we highlighted some sampling techniques:

Random Sampling

  • They randomly get selected quite well but hold some cons, like
  • Overlapping
  • For specific use-case, it won’t work

Stratified Sampling

  • This sampling is used when you want to target those certain groups that indulge most. For example, beauty products were this kind of company targeting women. When we gather the data, we avoid unnecessary categor

Systematic Sampling

  • Systematic sampling is a probability sampling method in which a random sample with a fixed periodic interval is selected from a larger population.
  • This method is efficient and more accessible to implement than simple random sampling in some cases.

Cluster Sampling

  • In cluster sampling, samples are selected randomly from clusters of the population.
  • It includes all the members of selected clusters.

Convenience Sampling

  • Convenience sampling involves choosing the easiest or most readily available individuals or items for the sample.
  • While quick and convenient, it can introduce bias and may not represent the population well.

Snowball Sampling

  • Snowball sampling is helpful in situations where it’s challenging to determine all population members.
  • It starts with an initial participant and relies on referrals to identify additional participants.

Purposive Sampling

  • Purposive sampling involves selecting specific individuals or items intentionally based on certain characteristics.
  • It is useful in qualitative research and may not be suitable for making generalizable inferences.

The measure of central tendency

Central Tendency is the summary of the data set that you calculate using Mean, Mode, and Median. It tells you the most average value and it’s also called “Center Location of Data”.

let me show a few examples for all.

  • Mean:

    • When the record holds values then mean is used. i.e. age = [ 33, 22, 55, 44, 55, 44 43] , mean = total age / number of records . 296 / 7 = 42.7
  • Median:

    • This method is efficient when the record holds outliers like the following.
      • For instance, age = [ 5, 4, 11, 15, 11, 9 90], where the average age is between 5-10 but because of 90 Mean value is 20.0
      • which is not valid, Median get center value, for odd center value and for even add (two center value) /2 from center, and for odd take center Median = 15 from above example.
  • Mode:

    • The record holds repeated values, then we pick that react value as our Mode. i.e. age = [2, 3, 5, 6, 7, 3, 3], Median = 3

Use of Statistical Methods in Machine Learning

Statistics plays a crucial role in various aspects of data analytics and machine learning:

Data Preprocessing

  • Statistical techniques help handle missing values, data cleaning, and determining outliers.
  • Descriptive statistics help you to summarize and understand the data’s distribution.

Feature Engineering

  • Statistical methods help create new components or modify existing ones.
  • Techniques like standardization and normalization ensure that features are on the same scale.

Model Training and Validation

  • Cross-validation approaches, such as k-fold cross-validation, use statistical principles to analyze performance and prevent overfitting.
  • Statistical tests help compare machine learning algorithms or models to select the best one.

Model Evaluation

  • Statistical metrics like recall, F1-score, accuracy, precision, and ROC-AUC quantify model performance.
  • Confidence intervals provide information about the uncertainty associated with model pred ictions.

Hypothesis Testing

  • It determines whether observed differences or relationships in the data are crucial for analyzing the statistics.
  • You can use machine learning to assess the significance of feature importance or model improvements.

Probability and Uncertainty

  • Probability theory is fundamental to Bayesian machine learning, where probabilistic models are used to capture uncertainty.
  • Bootstrapping is a statistical technique helpful in analyzing model parameters and forecast uncertainty.


Statistics plays a crucial role in machine learning by providing the mathematical foundation for data analysis, model training, and evaluation. Furthermore, it helps businesses to understand data distributions and to make informed decisions, assessing model performance and ensuring robust generalization. Overall, statistics is the backbone of machine learning algorithms.


By using algorithms, machine learning analyzes data, identifies patterns, and makes predictions or decisions without explicit programming. It involves generating models from data, optimizing them, and utilizing them to make predictions and decisions.

To use machine learning, first gather and preprocess data. Then, please select an appropriate algorithm, train a model on your data, validate it, and deploy it for prediction or decision-making tasks.

By learning from data, computers can make predictions or make decisions without explicitly programming them. Furthermore, it helps handle tasks like image recognition, recommendation systems, fraud detection, and more.

Machine learning is popular due to its ability to automate complex tasks, improve decision-making, and analyze vast datasets. Furthermore, its versatility has applications in various fields, driving its widespread adoption.

To learn machine learning algorithms, start with foundational math and statistics, then study supervised, unsupervised, and reinforcement learning concepts, practice with accurate data, and work on projects.