Types of Statistics Explained: Descriptive vs Inferential with Examples (Updated August 2026)

Types of Statistics Explained: Descriptive vs Inferential with Examples (Updated August 2026) (Updated August 2026)

Every machine learning project starts with data — and before you run a single algorithm, you need two types of statistical thinking. The NASSCOM-Deloitte report projects India needs 1.25 million AI professionals by 2027, and one of the most common gaps interviewers find is candidates who know libraries but cannot explain what their descriptive or inferential statistics actually mean. This guide, grounded in the ABC Trainings Proficient ML curriculum, breaks down both types of statistics clearly — what they measure, when to use them, and how they connect to the algorithms you'll apply in real projects.

What Are the Two Main Types of Statistics?

Statistics has two main branches. Descriptive statistics involve summarising and describing the features of a dataset — you're describing what you already have. Inferential statistics involve making predictions and drawing conclusions about a population based on sample data — you're inferring beyond what you directly measured. In machine learning, descriptive statistics are used during data exploration and preprocessing; inferential statistics are used to validate model hypotheses, measure significance and compare model performance. Every serious ML course teaches both, because each serves a different phase of the ML workflow.

Descriptive Statistics: Measures of Central Tendency

Central tendency measures describe where most data points are concentrated. Mean is the arithmetic average: sum all values and divide by count. Median is the middle value in a sorted dataset — it is not affected by extreme outliers. Mode is the most frequently occurring value — a dataset can have no mode, one mode (unimodal) or multiple modes (bimodal, multimodal). In practice, use mean when data is symmetric and outlier-free; use median when you have skewed distributions (income data, house prices); use mode when filling missing values in categorical columns. The ABC Trainings instructor tests students on recognising which measure is appropriate before writing any code.

Feature	Descriptive Statistics	Inferential Statistics
Purpose	Summarise existing data	Draw conclusions about a population
Key tools	Mean, median, mode, SD, skewness	Hypothesis test, CI, regression, ANOVA
When used in ML	EDA, preprocessing, feature understanding	Model validation, A/B testing, significance
Result	Charts, tables, summary numbers	Probability statements, decisions

Measures of Spread and Shape: Variance, Standard Deviation, Skewness and Kurtosis

Spread and shape measures go beyond the centre. Range is the difference between maximum and minimum — simple but affected by outliers. Variance measures average squared deviation from the mean; standard deviation is its square root, expressed in the original unit. Skewness measures whether data has a longer tail to the left (negative skew — mean < median) or right (positive skew — mean > median). Zero skewness means the distribution is symmetric. Kurtosis measures tail heaviness: leptokurtic distributions have heavy tails and sharp peaks (positive kurtosis); platykurtic distributions have light tails and flat peaks (negative kurtosis); mesokurtic is the normal distribution baseline (zero kurtosis). These shape measures matter in ML because many algorithms assume normally distributed features.

Data Visualisation in Descriptive Statistics: Histograms, Bar Charts and Box Plots

Descriptive statistics are communicated visually through charts. A histogram shows frequency distribution of a continuous variable — it reveals the shape (skewness) of the data at a glance. A bar chart compares counts or averages across discrete categories — for example, number of students enrolled per course. A pie chart shows proportional breakdown — useful for a quick composition view but misleading with many categories. A box plot simultaneously shows median, interquartile range (IQR), and outliers — it is one of the most information-dense tools in exploratory data analysis. Four-chart grid (histogram + box plot + bar + scatter) is the standard exploratory step in every serious ML project before modelling begins.

What Is Inferential Statistics and How Does It Work?

Inferential statistics allow you to take a sample, analyse it, and make reliable statements about the larger population it came from. The classic example: you cannot survey all Indian voters, so you survey 10,000 and infer national opinion. In machine learning, your training data is a sample; your model needs to generalise to the full population of unseen inputs. Inferential techniques — hypothesis tests, confidence intervals, regression analysis — are the tools that validate whether patterns in your sample are real or random. Without inferential thinking, you cannot reliably tell whether a model improvement is genuine or just random variation in your test set.

Hypothesis Testing, Confidence Intervals, Regression Analysis and ANOVA

Hypothesis testing starts with a null hypothesis (H0: no effect, no difference) and an alternative hypothesis (H1: there is an effect). You calculate a p-value — the probability of observing your result if H0 were true. If p < 0.05 (the standard significance level), you reject H0. A confidence interval gives a range of values within which the true population parameter lies with a specified confidence level — typically 95%. Regression analysis models the relationship between dependent and independent variables (covered in detail in later blog posts in this series). ANOVA (Analysis of Variance) compares means across multiple groups — for example, testing whether average salary differs across three job titles. These tools are used in A/B testing, model comparison and feature significance testing in industry ML projects.

Which Type of Statistics Should You Learn First for ML Jobs in Pune?

Start with descriptive statistics — it is the entry point to understanding your data before you can apply inferential techniques or ML algorithms. In the ABC Trainings ML programme, students complete descriptive and inferential statistics in the first three weeks, working with real datasets before any algorithm code is introduced. This order — statistics first, algorithms second — is what separates engineers who understand their models from those who just call library functions. Roles hiring ML freshers in Pune in 2026 include Data Analyst, Junior Data Scientist and ML Engineer Trainee, with starting salaries ranging from ₹4 LPA to ₹8 LPA depending on skill depth. Call 7039169629 or WhatsApp 7774002496 to check current batch availability at Wagholi or Hadapsar.

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 the difference between descriptive and inferential statistics?

Descriptive statistics summarise the data you have — mean, median, mode, variance and charts. Inferential statistics use a sample to draw conclusions about a larger population — through hypothesis tests, confidence intervals and regression. Both are used in ML: descriptive during exploration, inferential during model validation.

What is skewness and why does it matter in machine learning?

Skewness measures whether your data distribution has a longer tail on one side. Positive skew (right tail) means the mean is pulled above the median by high outliers. Negative skew (left tail) means the mean is below the median. Many ML algorithms perform best on symmetric (zero-skew) distributions, so knowing your data's skewness tells you whether to apply a log transformation before training.

What is a p-value in hypothesis testing?

A p-value is the probability of obtaining your observed result if the null hypothesis (no effect) were actually true. A low p-value (typically < 0.05) means the observed pattern is unlikely to be random, so you reject the null hypothesis. In ML, p-values are used to test whether a new feature genuinely improves model performance or whether the improvement happened by chance.

Do I need both types of statistics for a machine learning job?

Yes — both are essential. Descriptive statistics help you understand and preprocess your dataset. Inferential statistics help you validate whether your model's improvements are statistically significant and whether patterns generalise. Candidates who understand only one type struggle to explain their model decisions in technical interviews.