Interpreting Hypothesis Tests

Explore how to interpret hypothesis test results by examining p-values and significance levels. Understand the distinction between rejecting and failing to reject the null hypothesis, and learn about Type I and Type II errors through practical analogies. This lesson clarifies common misconceptions, helping you assess statistical evidence accurately and make informed conclusions.

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Two possible outcomes
Types of errors
How do we choose alpha?

Interpreting the results of hypothesis tests is one of the more challenging aspects of this method for statistical inference. Let’s understand the process and address some common misconceptions.

Two possible outcomes

Given a prespecified significance level $\alpha$ , there are two possible outcomes of a hypothesis test:

If the $p$ -value is less than $\alpha$ , then we reject the null hypothesis $H_0$ in favor of $H_A$ .
If the $p$ -value is greater than or equal to $\alpha$ , we fail to reject the null hypothesis $H_0$ ...

1.Getting Started with Data in R

2.Data Visualization

3. Data Wrangling

4.Data Importing and “Tidy” Data

5.Basic Regression

6.Multiple Regression

7.Statistical Inference with the infer Package

8.Bootstrapping and Confidence Intervals

9.Hypothesis Testing

10.Inference for Regression

Project

11. Tell a Story with Data

12.Appendix

Project

Interpreting Hypothesis Tests

Two possible outcomes