The significance level is a common term in statistics, especially in hypothesis testing. **It is the probability of rejecting the null hypothesis when it is true. **Since we are rejecting a correct null hypothesis means we are making some mistakes. Therefore it can be regarded as an error. It is a **Type 1** or **False Positive** Error.

The **Null Hypothesis** **(H0)** is the Hypothesis to be tested.

The **Alternate Hypothesis** **(H1)** is a contradictory statement to the Null Hypothesis.

## How to select the level of significance?

The level of significance or significance level is denoted by **α**. The most common values of **α **are **0.01**,** 0.05, **and **0.10 **which corresponds to **99%, 95%, and 90% **confidence in a test. The value of **α **is selected based on the certainty you need 0.01 > 0.05 > 0.10. If you want to be 95% confident then there is a 5% (100-95) risk of rejecting a null hypothesis that was true. The same applies to 90% where the significance level is 10% and 99% where the significance level is 1%.

The value of the significance level depends on the problem to which hypothesis testing is applied. A low significance value is used in cases for which high certainty is required and vice versa.

**For Example**, if you want to check if a machine is working properly or not, then you may go with a 0.01 significance level since you expect little or no mistakes.

For cases like analyzing human behavior, you may go with a 0.10 significance level. since human behavior can be very uncertain.

## False Positive Errors in Hypothesis Testing

In a binary classification problem where the target class is Positive and Negative. Let there be a case where the class is Negative but the model predicts it as a Positive, it is a False Positive or a Type 1 Error.

You can look at the below **confusion matrix** to understand the error better.

The probability of making a False Positive Error is α. Since the value of the significance level (α) is selected by you, the responsibility for making this error is on you.