Statistics Definitions > Stanine Score

## What is a Stanine Score?

A stanine (“standard nine”) score is a way to scale scores on a** nine-point scale**. It can be used to convert any test score to a single-digit score. Like z-scores and t-scores, stanines are a way to assign a number to a member of a group, relative to all members in that group. However, while z-scores and t-scores can be expressed with decimals like 1.2 or 3.25, stanines are always positive whole numbers from 0 to 9.

Stanines are also similar to normal distributions. You can think of these scores as a bell curve that has been sliced up into 9 pieces. These pieces are numbered 1 through 9, starting at the left hand section. However, where a standard normal distribution has a mean of 0 and a standard deviation of 1, stanines have a mean of 5 and a standard deviation of 2.

## What do Stanines mean?

A person with a score of 9 is in the top 4% of the scorers, while a person with a score of 1 is in the bottom 4%. These types of scores allow you to easily tell if a score is below the mean (a score of 5) or above the mean.

## How to Convert a Score to a Stanine

Step 1: Rank the scores from lowest to highest.

Step 2: Assign a stanine score to your scores from Step 1:

Stanine score | Percentage of scores |

1 | Bottom 4% |

2 | Next bottom 7% |

3 | Next bottom 12% |

4 | Next Bottom 17% |

5 | Middle 20% |

6 | Next top 17% |

7 | Next top 12% |

8 | Next top 7% |

9 | Top 4% |

The mean lies in the middle of the fifth stanine, cutting the center 20% into two parts.

## Loss of Information

Stanines are a very simple way of categorizing items into top, middle and bottom percentages. This simplicity means that it’s a very imprecise way to measure anything. Everyone in the same stanine receives the same score. For example, a person at the bottom of the 5th is almost 20 percentage points below the person at the top of the 5th. These differences are what is called “loss of information.”

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*Statistical concepts explained visually* - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

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