# Percentile over Average/Mean

The average comes from mathematics. The mean comes from statistics. It doesn’t matter in the context of this post. What matters is… percentile comes from usefulness.

When analysing or displaying the data in the dashboard, we often use Average.

Imagine cycle times of 10 user stories:

Story_1 = 3 hours

Story_2 = 4 hours

Story_3 = 5 hours

Story_4 = 5 hours

Story_5 = 6 hours

Story_6 = 7 hours

Story_7 = 7 hours

Story_8 = 10 hours

Story_9 = 75 hours

Story_10 = 80 hours

## Average

Average = (3+4+5+5+6+7+7+10+75+80) / 10 = 20.2

The conclusion might be that the average time it takes to get something done after we start working on it is 20.2 hours.

But maybe Story_9 and Story_10 had external dependencies that forced us to wait. And it is more like an exception, not a standard?

That’s better to move forward than to start optimising the (20.2 hours) number.

## Percentiles

Let’s try to use percentiles.

The Percentile function works in a way:

It sorts the dataset.

It splits the whole dataset in a place set by a param of the percentile.

So if a dataset has 10 items and we look for the 50th percentile, we split that after the 5th element.

And we get a value between the 5th and 6th elements.

In fact, the 50th Percentile is a median.

Examples based on our dataset above:

50th percentile (median, between Story_5 and Story_6) = 6.5 hours

70th percentile (between Story_6 and Story_7) = 7.9 hours

90th percentile (between Story_9 and Story_10) = 75.5 hours

## How do we read that?

Cycle time of 50% of the stories is less than or equal to 6.5 hours.

Cycle time of 70% of the stories is less than or equal to 7.9 hours.

Cycle time of 90% of the stories is less than or equal to 75.5 hours.

With that data, making more accurate decisions with the team is easier.