Type I and type II errors
Type I and Type II errors are mistakes that can happen when we make decisions based on data.
Type I errors happen when we think something is true (that it is "positive"), but it is actually false (it's "negative"). For example, imagine you're looking at a test that tells you if you have a certain disease. You might think the test says you are positive for the disease (it's true), but it might actually be saying you are negative for the disease (it's false). This is a Type I error.
Type II errors happen when we think something is false (it's "negative"), but it is actually true (it's "positive"). For example, imagine you're looking at a test that tells you if you have a certain disease. You might think the test says you are negative for the disease (it's false), but it might actually be saying you are positive for the disease (it's true). This is a Type II error.
Both Type I and Type II errors are very important to avoid in decision-making. By understanding the probabilities of both of these mistakes, it can help us make better decisions.
Type I errors happen when we think something is true (that it is "positive"), but it is actually false (it's "negative"). For example, imagine you're looking at a test that tells you if you have a certain disease. You might think the test says you are positive for the disease (it's true), but it might actually be saying you are negative for the disease (it's false). This is a Type I error.
Type II errors happen when we think something is false (it's "negative"), but it is actually true (it's "positive"). For example, imagine you're looking at a test that tells you if you have a certain disease. You might think the test says you are negative for the disease (it's false), but it might actually be saying you are positive for the disease (it's true). This is a Type II error.
Both Type I and Type II errors are very important to avoid in decision-making. By understanding the probabilities of both of these mistakes, it can help us make better decisions.