What Is Statistics in Data Science? Population, Sample, Sampling Methods and Core Concepts Explained (Updated August 2026)

What Is Statistics in Data Science? Population, Sample, Sampling Methods and Core Concepts Explained (Updated August 2026) (Updated August 2026)

Before you write a single line of machine learning code, you need to understand statistics — because every algorithm, from linear regression to neural networks, is built on statistical foundations. The NASSCOM-Deloitte report projects India needs 1.25 million AI and data professionals by 2027, yet most aspirants skip the foundational layer and jump straight to frameworks. That gap is exactly what gets candidates eliminated at interview screening. In this guide, grounded in ABC Trainings' Proficient ML curriculum, you'll learn what statistics actually is, the key terminologies every ML practitioner uses daily, and how statistical thinking connects to the algorithms you'll eventually build.

What Is Statistics and Why Does Every Machine Learning Engineer Need It?

Statistics is the field that involves collecting, analysing, interpreting, presenting and organising data. In machine learning, every algorithm uses statistical principles to make sense of numbers and find patterns. When a data scientist calculates model accuracy, measures error, or tests whether a new feature improves performance, they are applying statistics. The instructor in ABC Trainings' ML course explains it directly: before we can solve a regression or classification problem, we need to understand the data we're working with — its shape, spread, and central values. Statistics gives you that understanding. Its two main branches are descriptive statistics (summarising what is in your dataset) and inferential statistics (drawing conclusions about a larger population from a sample).

Key Terminologies: Population, Sample, Variable and Data

Before running any ML algorithm, you need to understand four foundational terms. Population is the complete group of items you're studying — for example, all customers of an e-commerce platform. Sample is a subset of the population that you actually work with — because collecting data on every member of a population is usually impractical. Variable is any characteristic of the items in your dataset that can vary — height, weight, age, purchase amount, churn status. Data is the collection of observed values for those variables. A confusion common among beginners: the population is what you want to know about; the sample is what you actually measure. Statistical inference is the process of using the sample to make reliable statements about the population.

Measure	What it tells you	Used in ML for
Mean	Average value	Loss calculation, imputation
Median	Middle value, outlier-robust	Skewed data imputation
Mode	Most frequent value	Categorical missing value fill
Variance	Average squared deviation	Feature selection, PCA
Standard Deviation	Spread in original units	Z-score scaling, outlier detection

Measures of Central Tendency: Mean, Median and Mode

Central tendency measures tell you where the middle of your data sits. Mean is the arithmetic average — sum all values and divide by the count. Median is the middle value when data is sorted — for even counts, average the two middle values; when mean and median are equal, the data is in arithmetic progression. Mode is the value that appears most frequently — a dataset can be unimodal (one peak), bimodal (two peaks) or multimodal (multiple peaks). In machine learning, mean is used in loss calculations, median is used when outliers distort the mean (median is robust), and mode is used for categorical data imputation. Understanding which measure to use in which situation is a skill tested in every data science interview.

Measures of Spread: Range, Variance and Standard Deviation

Spread tells you how far data points sit from the centre. Range is simply max minus min — fast to calculate but sensitive to outliers. Variance measures the average squared deviation from the mean. You calculate it by: subtracting the mean from each value, squaring the result, summing all squared deviations, then dividing by n (population) or n-1 (sample). Standard deviation is the square root of variance — it brings the spread back to the original unit of measurement, making it interpretable. A high standard deviation means data is spread wide; a low one means values cluster tightly around the mean. In ML, standard deviation is used in feature scaling (z-score normalisation), outlier detection (3-sigma rule), and evaluating model consistency across cross-validation folds.

Sampling Techniques Used in Machine Learning Data Collection

When you cannot measure an entire population, you take a sample — and how you take that sample affects whether your results are trustworthy. Random sampling gives every member an equal chance of selection, eliminating bias. Stratified sampling divides the population into subgroups (strata) and samples from each proportionally — useful when subgroups differ significantly. Systematic sampling selects every n-th person from a sorted list. Cluster sampling divides the population into geographic or natural groups and randomly selects entire clusters. Convenience sampling picks whoever is easiest to reach — fast but prone to bias. In ML projects, poor sampling produces biased training data, which leads to models that perform well in testing but fail in production. This is why data collection strategy is discussed in every serious ML course.

Data Visualisation, Probability and the Bridge to ML Algorithms

Data visualisation uses charts and graphs to communicate statistical findings. A histogram shows the frequency distribution of a continuous variable — it reveals skewness (whether data is left-tailed or right-tailed) and kurtosis (peak sharpness). A bar chart compares discrete categories. A pie chart shows proportions. A box plot displays median, quartiles and outliers simultaneously — one of the most information-dense charts in data science. Probability is the likelihood of an event occurring, ranging from 0 (impossible) to 1 (certain). Probability is what connects statistics to ML: logistic regression outputs probabilities, Naïve Bayes is built entirely on probability, and even neural network outputs are probability distributions. Hypothesis testing uses p-values to decide whether patterns in a sample are real or happened by chance — an essential skill for validating any ML experiment.

Statistics Training for ML Careers at ABC Trainings Pune

The ABC Trainings Proficient Machine Learning course covers statistics from first principles before introducing any algorithm. Students work through mean, variance, sampling and hypothesis testing with real datasets before touching sklearn or pandas. This approach ensures you understand what the library is calculating, not just how to call the function. After completing the statistics and probability modules, students move to supervised and unsupervised learning algorithms with the mathematical confidence to debug model behaviour. Batches run in Wagholi and Hadapsar. Fees start at ₹25,000 for the full ML programme; CMKPY-eligible students can claim ₹6,000–₹10,000 reimbursement. Call 7039169629 or WhatsApp 7774002496 to check current batch dates and CMKPY eligibility.

Eligible students can apply for CMKPY (Chief Minister Yuva Karyaprasaran Yojana) skill training reimbursement of ₹6,000–₹10,000 toward approved ML and data science courses. Ask ABC Trainings whether the current ML batch is CMKPY-empanelled when you enquire.

FAQs

What is statistics in simple terms for a machine learning beginner?

Statistics is the science of collecting, analysing and interpreting numerical data to find patterns and draw conclusions. In machine learning, statistical measures like mean, variance and probability are the mathematical engine behind every algorithm from linear regression to deep learning.

What is the difference between population and sample in statistics?

Population is the complete group you want to study — for example, all users of an app. Sample is a subset of that population that you actually collect data from, because studying every member of a large population is impractical. In ML, your training dataset is a sample, and the goal is to build a model that performs well on the full population.

Why is standard deviation important in machine learning?

Standard deviation measures how spread out your data is from the mean. In ML, it is used in z-score normalisation (scaling features so no single feature dominates), in outlier detection (the 3-sigma rule), and in evaluating model consistency across cross-validation splits. A model with high variance in cross-validation scores is overfitting.

Do I need to study statistics before starting a machine learning course?

Yes. Statistics is not optional for ML — it is the foundation. Without understanding mean, variance, probability and sampling, you cannot interpret why a model is failing, how to preprocess data correctly, or what evaluation metrics actually measure. All serious ML training programmes at ABC Trainings cover statistics and probability before introducing algorithms.