# Metrics

Inspector offers several ways to view and aggregate data: Execution time, Memory usage, Performance distribution. 

What do they do, and how should you use them? 

### Data aggregations

Data aggregation is when multiple values of a specific parameter are grouped together to give a single summary value. This is especially useful when you want to extract simple but meaningful information from your application data that consists of thousands, or even millions of measurements.

### Execution time & Memory Usage

These metrics are calculated using the 75th percentile. It is the value where 75% of all measurements are under it, and 25% of measurements are over it. It is [the percentile that Google research team recommends](https://web.dev/defining-core-web-vitals-thresholds/#choice-of-percentile) using when monitoring Web Vitals.

![](/files/ru6upgdabWqBPLI25PRx)

The chart above shows the page load histogram with all of the measurements below the 75th percentile coloured in blue.

#### Why use the 75th percentile

The 75th percentile is a good balance of representing the vast majority of measurements, and not being impacted by outliers. While not as stable as the median (50th pecentile), the 75th percentile is a good choice for seeing medium - to long term trends. We also think the 75th percentile is the best value to use when setting performance budgets.

### Performance Distribution

You can make a deeper investigation on specific transactions using the frequency distribution histogram.

That's a fancy way of describing a bar chart where the X (horizontal) axis shows the value of a measurement and the Y (vertical) axis shows the number of measurements that had that value. 

Take this chart for example:

![Performance Distribution Chart](/files/ImKOAQ3cYEO2eHVs3PYl)

The chart above shows clusters of execution times on the X axis. The height of the bars represents how many measurements had execution times that fell within each cluster. We can see that the majority of execution times were between 0 and 407 milliseconds, with a smaller number of execution times on either side.

Notice how there are many clusters to the right of the chart, but they all have a small number of measurements. This is called the "long tail" and represents the times the transaction had the worst performances.

{% hint style="info" %}
This particular chart is "**clickable**". So you can view and compare two samples of the transaction at different performance. One from a good performance cluster (left side of the chart), versus one from bad performance (right side of the chart).
{% endhint %}


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