## Interpreting Results

### Dashboard

Your dashboard is intended to give you a view of everything happening in your experiments at a glance.

Think of the metrics displayed here as the vital signs of your experiments:

If you click on the Top Performing Variation for any dashboard item, you will be brought to that experiment’s Full Report. Reports display raw data, as well as metrics related to test performance and significance:

### Raw Data

**Conversions** is the total number of users that converted.

**Events** is the total number of events users completed.

**Time** is the total time users spent in a session, on a screenview, etc.

**Quantity** is the total quantity of something occurring in the app

**Users** is the number of users included in the experiment to date.

### Performance Metrics

**Conversion Rate** is a measure for **Conversion Goals** and is calculated as:

Unique Goals Reached / Users

**Average Events per User** is a measure for **Event Goals** and is calculated as:

Total Events / Users

**Average Time per User** is a measure for **Timing Goals** and is calculated as:

Total Time / Users

**Average Quantity per User** is a measure for **Quantity Goals** and is calculated as:

Total Quantity / Users

**Observed Improvement** is the percent change of each variation’s performance compared to the baseline. It is calculated as:

__(New Variation’s Performance) – (Baseline Variation’s Performance)__

Baseline Variation’s Performance

* 100

### Significance Metrics

**Confidence Intervals** represents the range in which there is a 95% probability that the variation’s true performance lies. Confidence Intervals can be found in numerical form underneath the variation performance metric, and represented graphically by the syringe graph to the right of the variation performance metric. It is calculated as:

For rate goals:

+/- 1.96 * sqrt (p * (1 – p) / n)

For non-rate goals:

+/-1.96*(stdev/sqrt(n))

**Chance to Beat Baseline** is the probability that the Observed Improvement is statistically significant. It is calculated as the p-Value associated with the following z-statistic:

__New Variation’s Performance – Baseline Variation’s Performance__

sqrt (Standard Error of New Variation^2 + Standard Error of Baseline Variation^2)

**Estimated Users to Completion** is an estimate of the number of new test participants needed to arrive at a statistically significant result, where the top-performing variation’s Chance to Beat Baseline is greater than or equal to 95%.