Name: Type I error vs Type II error
Uploaded: 2020-03-09T00:40:34.000Z
Duration: 3 min 31 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

In this lesson, we will learn about the errors that can be made in hypothesis testing.

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

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

We should also mention that the probability of rejecting a false null hypothesis is equal

This is the researcher’s goal – to reject a false null hypothesis.

Therefore, 1 minus beta is called the power of the test.

Generally, researchers increase the power of a test by increasing the sample size.

This is a common table statisticians use to summarize the types of errors.

Now, let’s see an example that I heard from my professor back when I was studying statistics

You are in love with this girl from the other class, but are unsure if she likes you.

First, if she likes you back and you don’t invite her out, you are making the type I

The null hypothesis in this situation is: she likes you back.

It turns out that she really did like you back.

Unfortunately, you did not invite her out, because after testing the situation, you wrongly

In other words, you made a type I error - you rejected a true null hypothesis and lost your

It is a very serious problem, because you could have been made for each other, but you

She doesn’t like you back, but you go and invite her out.

The null hypothesis is still: she likes you back, but this time it is false.

In reality she doesn’t really like you back, that is.

However, after testing, you accept the null hypothesis and wrongly go and invite her out.

She tells you she has a boyfriend that is much older, smarter and better at statistics

You made a type II error – accepted a false null hypothesis.

However, it is no big deal, as you go back to your normal life without her and soon forget

You don’t really want to make any of the two errors, but it happens sometimes.

You should be aware that statistics is very useful, but not perfect.

That’s all from our love slash life slash statistics lesson.

Subtitles ListPlay Video

Type I error vs Type II error

awkward

situation

positive

aware