In statistical testing, there can be incorrect outcomes. These outcomes are classified into type I and type II errors. Here, type I error is a false positive and type II error is a false negative. An incorrect rejection of a true null hypothesis (*H*_{0}) is a type I error. An incorrect failed rejection of a false null hypothesis (*H*_{0}). Lets consider an example of a fire alarm while making a null hypothesis (*H*_{0}) that there is no fire.

### Type I error (false positive)

A fire alarm going off when there is no fire **(false alarm)**.

**Test:** Is there a fire?

**Result (alternative hypothesis):** Yes (Alarm sounded)

**Null hypothesis ( H_{0}):** No

**Actual result:**No (true null hypothesis)

In this case, the null hypothesis (

*H*

_{0}) is that

*there is no fire*and there actually was no fire, so this is a true null hypothesis. Since, we sounded the alarm and incorrectly rejected the true null hypothesis. This is a type I error.

### Type II error (false negative)

No alarm going off when there is a fire.

**Test:** Is there a fire?

**Result (alternative hypothesis):** No (No alarm sounded)

**Null hypothesis ( H_{0}):** No

**Actual result:**Yes (false null hypothesis)

In this case, the null hypothesis (

*H*

_{0}) is that

*there is no fire*and there actually was a fire, so this is a false null hypothesis. Since we did not sound the alarm and incorrectly failed to reject (accepted) the false null hypothesis. This is a type II error.

Here, is a table that shows the outcomes of a statistical test [0]. The correct outcomes are shown in green, and the incorrect outcomes are the type I and type II errors.

Alternative Hypothesis | |||
---|---|---|---|

Positive | Negative | ||

Actual Result |
Positive | True Positive | True Negative (Type II error) |

Negative | False Positive (Type I error) | False Negative |

Next >> Statistical significance

**References**:

[0] Liu, Bing. *Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media, 2007*. p-81

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