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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Descriptive vs Inferential Statistics: What’s the Difference?

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Analytics Vidhya Last Updated : 30 May, 2023
6 min read

Statistics involves gathering and organizing raw data into an easily understandable format. This field of mathematics helps analyze data and apply the findings to address various data-related issues. There are two broad categories of statistical methods: descriptive and inferential statistics. This article will explain the difference between descriptive vs inferential statistics.

Table of contents

Descriptive vs Inferential Statistics

Descriptive StatisticsInferential Statistics
The area of statistics known as descriptive statistics is focused on providing a description of the population being studied.A kind of statistics known as inferential statistics concentrates on inferring information about the entire population from observation and sample analysis as well.
It provides details about the raw data, which in some way characterizes the data.Using information obtained from the population, it draws conclusions about the population.
It aids in the collection, organization, analysis, and meaningful presentation of data.It enables data comparison, hypothesis testing, and forecast-making.
It is restricted to a sample or population with a modest size and explains already-known facts.It makes an effort to draw a conclusion regarding the population.
It may be done using graphs, tables, charts, and other visual aids.Probability can help achieve it.
Descriptive vs Inferential Statistics
Source: Science Struck

What is Descriptive Statistics?

Descriptive statistics refers to techniques used to enumerate and characterize a dataset’s key characteristics, such as the variability, central tendency and distribution. These techniques offer a summary of the data and aid in discovering trends and linkages. 

A few key takeaways about Descriptive Statistics are as follows:

  • Descriptive statistics summarize or describe the characteristics of a data collection.
  • It involve three primary types of measures: measures of central tendency, measures of variability (or spread), and measures of frequency distribution.
  • Measures of central tendency (such as mean, median, and mode) describe the center of the data set.
  • Measures of frequency distribution describe the occurrence or frequency of data within the data collection.
Descriptive Statistics
Source: international Six Sigma Institute

What is Inferential Statistics?

A statistical technique called inferential statistics uses analytical methods to make inferences about a population by analyzing samples taken at random. For example, generalizations about a population. 

A few key takeaways about Inferential Statistics are as follows:

  • Analyzing samples from population data and employing inferential statistics helps in developing a solid understanding of the data.
  • Utilizing various analytical techniques facilitates the creation of population-level generalizations.
  • Researchers employ different sampling procedures to select random samples that accurately represent the population.
Inferential Statistics
Source: Slide Team

Also Read: Data Science vs Data Analytics: Which One Will Give You the Edge in 2023?

Types of Descriptive Statistics

Using descriptive statistics, you may describe your data in terms of its characteristics. Descriptive statistics often fall into one of three categories:

Frequency Distribution

Frequency distribution represents the occurrence of an event or element and is utilized for analyzing qualitative and quantitative data. It documents and presents the information in a tabular format.

For instance, consider a school that arranges an annual picnic for a group of students. Some students have visited the picnic area multiple times, while others are returning after a previous visit. The students are categorized based on the number of visits they have made. In this case, the frequency distribution among the students is determined by the number of visits.

Central Tendency

There are three ways to calculate central tendency:

  • Mean
  • Median
  • Mode

The results capture the essence of data collection, a sum of all counts or occurrences. Mean refers to the average value that occurs most frequently, the median designates the middle or center score of the data sample, and the mode signifies the most frequent number.

Variability

Variability is the degree of dispersion between data points, and it is a fundamental statistical concept that provides insights into the data’s spread, distribution, or inconsistency. 

For instance, there has only ever been one visit to the picnic area, and four trips to picnic areas are the most. The variability that forms a range determines each value’s distance from the central tendency, and the degree of dispersion is the range itself.

Types of Inferential Statistics

Regression Analysis

Regression analysis predicts how one variable will change with another. These models that can be employed include ordinal, logistic, nominal, basic linear, and multiple linear models. The most common type of regression used in inferential statistics is linear regression. Linear regression investigates the response of the dependent variable to a unit change in the independent variable.

Hypothesis Testing

Hypothesis testing is statistical reasoning using information from a sample to conclude a population probability distribution. The models for hypothesis testing include the following resources:

TestSample SizeDistributionPurpose
Z-test≥ 30NormalInvestigate population mean
T-test< 30t-distributionInvestigate population mean
F-testAnyAnyCompare variances of two samples/populations

Also Read: What is the Difference Between Data And Information?

Descriptive vs Inferential Statistics – Examples

Please note that while the general guidelines for using each test are mentioned, there may be specific conditions and assumptions that need to be met for each test to be valid.

Descriptive Statistics Examples

  • Class A students scored {70, 85, 90, 65}, and class B students received scores of {60, 40, 89, 96}. The mean is used to calculate the average marks for each class: class A has an average of 77.5, while class B has an average of 71.25. This indicates that class A has a higher average than class B.
  • The range is used to assess the difference between the most extreme scores. In the given example, Range A is 25, and Range B is 56, showing that Range B is greater than Range A.

Inferential Statistics Examples

  • Using a sample of 100 students, inferential statistics can estimate the average grade of the entire nation’s student population.
  • A coach wants to determine how many cartwheels sophomores at his institution can perform. A sample of a few individuals will execute cartwheels, and inferential statistics will be used to conclude the average number of cartwheels sophomores can do.

Descriptive vs Inferential Statistics – Advantages and Limitations

Descriptive Statistics

AdvantagesDisadvantages
We can show the data visually with descriptive statistics. Visual data presentation makes information much concise and simple to comprehend.Based on the provided data values, we cannot use descriptive statistics to make any prediction. 
We can highlight the key features of the data using the various statistical metrics.The process of gathering data utilizes time and money. For instance, running a survey requires a lot of labor to gather data.

Inferential Statistics

AdvantagesDisadvantages
As a result, we may conserve time and resources by concluding the entire population from a small sample size. Based on a sample, we drew estimations and conclusions, although they required greater accuracy. As we are not examining the entire population, there will always be a certain degree of uncertainty.
Based on historical values, we may anticipate the future values that a dependent variable will take using inferential statistical techniques like linear regression. We assume a normal distribution in the parent population to serve as the basis for the majority of hypothesis tests concerning the equality of means and variances.

Real Life Example – Descriptive vs Inferential Statistics

Descriptive StatisticsInferential Statistics
Political polling is considered a descriptive statistic as it presents actual data, which are the respondents’ responses, without making any inferences. Polls include questions like, “Whom did you vote for as President in the most recent election?” to gather information.Many people think that girls’ development is stunted by early gymnastics participation, and it is impossible to gather information on every female gymnast to respond. Even so, you may test the null hypothesis that “female gymnasts and non-gymnosophist do not differ in height as adults” using a sample of gymnasts using the central limit theorem and inferential statistics.

Checkout: Data Scientist vs Data Analyst: Which is a Better Career Option to Pursue in 2023?

Final Verdict – Descriptive vs Inferential Statistics

Descriptive and inferential statistics are most frequently used in combination, even though we have only presented them in a binary manner. These potent statistical methods work well together and form the basis of data analytics. If you are just starting out your career journey to Data Analyst, then our free courses will help you a lot in upskilling. Check out all our courses now!

Frequently Asked Questions 

Q1. What is the difference between descriptive and inferential statistics?

A. The properties of data collection are enumerated via descriptive statistics. You can test a hypothesis using inferential statistics or determine whether your data can be applied to a larger population. That is the key difference between descriptive and inferential statistics.

Q2. What are descriptive and inferential statistics with examples?

A. Let’s say you have access to the grades of 100 students from a particular nation. It is necessary to assess these kids’ performance. By itself, this information will provide only some valuable results. But descriptive statistics may be used to determine the distribution of the marks, providing a clear picture of each student’s performance.

Imagine you need to check the student’s test results from a particular nation. Inferential statistics are used to conclude the population using a sample of, say, 100 students.

Q3 What is an example of an inferential statistic?

A. Estimating populations (for instance, the mean score of all 11th graders) is an example of inferential statistics.

Also, testing hypotheses to make population-level inferences (such as the association between scores and family income).

Q4. What is an example of a descriptive statistic?

A. The Grade Average of a student is a perfect illustration of descriptive statistics. A GPA compiles the data generated by a wide range of grades, classes, and examinations, averages them together, and then provides a broad idea of the students’ academic achievement.

AverageDescriptive statisticsdistributionGuideInferential Statisticsrangesamplestatistics
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