Big Data PR

Type I error vs Type II error

In general, we can have two types of errors – type I error and type II error.

Sounds a bit boring, but this will be a fun lecture, I promise!

First we will define the problems, and then we will see some interesting examples.

Type I error is when you reject a true null hypothesis and is the more serious error. It is also called ‘a false positive’. The probability of making this error is alpha – the level of significance. Since you, the researcher, choose the alpha, the responsibility for making this error lies solely on you.

Type II error is when you accept a false null hypothesis. The probability of making this error is denoted by beta. Beta depends mainly on sample size and population variance. So, if your topic is difficult to test due to hard sampling or has high variability, it is more likely to make this type of error. As you can imagine, if the data set is hard to test, it is not your fault, so Type II error is considered a smaller problem.

Data Science PR

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