Module 1: Hypothesis Testing (t-tests) and Confidence Intervals
Module Overview
In this module, we're going to build on the descriptive statistics concepts we've already learned about as we started to explore our data. This module will introduce the idea of a "hypothesis test" and how we implement one, specifically using the t-test and t-distributions. We'll also cover how to calculate p-values and use the results to interpret our hypothesis.
In addition, this module will cover one of the most important concepts in statistics: the Central Limit Theorem. We'll learn about the properties of sampling distributions and how to interpret the expected mean of a sample distribution, which will, in turn, lead to the idea of confidence intervals and how we know the confidence level of our results and predictions.
Learning Objectives
- Explain the Purpose of a t-test and Identify Applications
- Set up and run one-sample and two-sample t-tests
- Draw conclusions with null and alternative hypotheses
- Explain the concepts of statistical estimate, precision, and standard error as they apply to inferential statistics
- Explain the implications of the central limit theorem in inferential statistics
- Explain the purpose of and identify applications for confidence intervals
Guided Project
Open DS_121_ttests_confidence_intervals.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 1 assignment to practice hypothesis testing and confidence intervals you've learned. The assignment covers formulating hypotheses, performing t-tests, calculating p-values, and interpreting statistical significance.