Statistical Inference is a branch of mathematics which quantifies making inferences about a population, using data drawn from the population with some form of random sampling.

Statistical Inference includes concepts such as parameter estimates, confidence intervals, and sampling distributions. These should be familiar concepts from your previous applied statistics courses.

Statistics plays an important role in machine learning by providing a framework of how to evaluate the accuracy of algorithms on new data. It is also critical for a process known as hyperparameter tuning, which we will cover in future lessons.

This week, we will be watching a video from zstatistics.com which reviews the core concepts from statistics, including data types, parameter estimation, confidence intervals, and sampling distributions.

We will also explore these concepts through engaging interactive resources from Brown University - Seeing Theory.

# Statistics Review

The video below is an excellent review of important statistics concepts by Justin Zeltzer at zstatistics.com

## Interactive Demonstrations

The interactive tool from Brown University below describes and simulates many of the statistical concepts in the previous video. Have a look at the different simulations to see these concepts in action.

# Next Steps

Please head over to the R tutorial to learn about random sampling and probability distributions in `R`

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