When using automated processes to make decisions, you need to be aware of how this automation can lead to mistakes. Computer programs can be as fallible as the humans who design them. Because of this, there is a responsibility to understand what can go wrong and what can be done to contain these foreseeable problems.
In statistical hypothesis testing, there are two types of error. A Type I error occurs when a hypothesis test finds a correlation between things that are not related. This error is sometimes called a “false positive” and occurs when the null hypothesis is rejected even though it is true.
For example, consider the history and chemistry major experiment from the previous exercise. Say you run a hypothesis test on the sample data you collected and conclude that there is a significant difference in interest in volleyball between history and chemistry majors. You have rejected the null hypothesis that there is no difference between the two populations of students. If, in reality, your results were due to the groups you happened to pick (sampling error), and there actually is no significant difference in interest in volleyball between history and chemistry majors in the greater population, you have become the victim of a false positive, or a Type I error.
The second kind of error, a Type II error, is failing to find a correlation between things that are actually related. This error is referred to as a “false negative” and occurs when the null hypothesis is not rejected even though it is false.
For example, with the history and chemistry student experiment, say that after you perform the hypothesis test, you conclude that there is no significant difference in interest in volleyball between history and chemistry majors. You did not reject the null hypothesis. If there actually is a difference in the populations as a whole, and there is a significant difference in interest in volleyball between history and chemistry majors, your test has resulted in a false negative, or a Type II error.
Instructions
In notebook.Rmd you will find four vectors: actual_positive, actual_negative, experimental_positive, and experimental_negative. These vectors represent outcomes from a statistical experiment.
The base R intersect() function can take two vectors as arguments and returns a vector containing the common elements.
Use the intersect() function and the vectors provided to define type_i_errors. This vector should contain the false positives of the experiment. View type_i_errors.
Now, define type_ii_errors, the list representing the false negatives of the experiment.