About this courseSkip About this course
This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly?
Using real-world examples from a wide array of domains including law, medicine and football, you’ll learn how data scientists make conclusions about unknowns based on the data available. Often, the data we have is not complete, yet we’d still like to draw inferences about the world and quantify the uncertainty in our conclusions. This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets.
Notably, you will develop the practice of hypothesis testing—comparing theoretical predictions to actual data, and choosing whether to accept those predictions. This method allows us to evaluate theories or hypotheses about how the world works.
You will also learn how to quantify the uncertainty in the conclusions you draw from hypothesis testing. This helps assess whether patterns that appear to be present in the data actually represent a true relationship in the world, or whether they might merely reflect random fluctuations due to noise. Throughout this course, we will go over multiple methods for estimation and hypothesis testing, based on simulations and the bootstrap method. Finally, you will learn about randomized controlled experiments and how to draw conclusions about causality.
The course emphasizes the conceptual basis of inference, the logic of the decision-making process, and the sound interpretation of results.
What you'll learnSkip What you'll learn
- The logical and conceptual frameworks of statistical inference
- How to conduct hypothesis testing, permutation testing, and A/B testing
- The purpose and power of resampling methods
- Relations between sample size and accuracy
- P-values, quantifying uncertainty, and generating confidence intervals using the bootstrap method
- How to interpret the results from hypothesis testing
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