Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Statistical Inference
This course is part of multiple programs.
Instructors: Brian Caffo, PhD
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What you'll learn
Understand the process of drawing conclusions about populations or scientific truths from data
Describe variability, distributions, limits, and confidence intervals
Use p-values, confidence intervals, and permutation tests
Make informed data analysis decisions
Skills you'll gain
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There are 4 modules in this course
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
What's included
10 videos11 readings1 assignment5 programming assignments
We're going to tackle variability, distributions, limits, and confidence intervals.
What's included
10 videos4 readings1 assignment3 programming assignments
We will be taking a look at intervals, testing, and pvalues in this lesson.
What's included
11 videos5 readings1 assignment3 programming assignments
We will begin looking into power, bootstrapping, and permutation tests.
What's included
9 videos4 readings1 assignment3 programming assignments1 peer review
Instructors
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Recommended if you're interested in Probability and Statistics
University of Illinois Urbana-Champaign
University of Illinois Urbana-Champaign
University of Washington
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Reviewed on Mar 21, 2017
The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.
Reviewed on Jan 30, 2016
I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference
Reviewed on Sep 6, 2022
Quite useful to most scientists that rely on data (real/from simulations) to draw conclusions. The fact that the course was generic and widely applicable to all fields was the highlight!
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