May 1, 2024
Updated May 8, 2025
34 minute read
Inferential statistics is a branch of statistics that allows us to make predictions or inferences about a larger group (a population) based on data collected from a smaller group (a sample). Unlike descriptive statistics, which simply summarizes the characteristics of a data set, inferential statistics helps us to test hypotheses, draw conclusions, and make predictions about the broader population from which the sample was taken. Imagine trying to find out the average height of all adults in a country. Measuring everyone would be impossible. Instead, you would measure a smaller, representative group and use inferential statistics to estimate the average height of all adults in that country.
my4nkt|
Find a path to becoming a Inferential Statistics. Learn more at:
OpenCourser.com/topic/my4nkt/inferential
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Inferential Statistics.
This classic textbook provides a comprehensive treatment of mathematical statistics, including a rigorous foundation for inferential methods and a wide range of applications.
This highly influential book covers a wide range of statistical learning methods, including inferential techniques for supervised and unsupervised learning.
A widely used textbook for undergraduate courses, this book covers a broad range of statistical methods, including inferential procedures and data analysis techniques.
Introduces Bayesian methods for social science research, covering topics such as Bayesian modeling, hierarchical models, and Markov chain Monte Carlo (MCMC) methods.
Provides an accessible introduction to causal inference, explaining the fundamental concepts and methods for drawing causal conclusions from observational data.
This advanced textbook focuses on Bayesian modeling and causal inference in the presence of incomplete data, providing methods for handling missing data and non-response.
This engaging and accessible book introduces the fundamental principles of statistical inference through real-world examples and case studies, making it a valuable resource for understanding the practical applications of inferential statistics.
This specialized book covers advanced topics in inferential statistics, such as generalized linear models, nonlinear regression, and nonparametric methods.
Provides a practical guide to inferential methods for data science, emphasizing the importance of understanding the underlying assumptions and limitations of statistical models.
Introduces Bayesian inferential methods, providing a coherent framework for incorporating prior knowledge and updating beliefs in light of new data.
This advanced textbook focuses on the analysis of time series data, providing methods for inferring causality and understanding the dynamic relationships between variables.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/my4nkt/inferential
Save
