Within Amplitude Experiment, the **Experiment Analysis** view is where you’ll find the details of your experiment. Visible on the *Analysis* card under the *Analyze* tab, it gives you a convenient way to quickly take in the most important, high-level statistical measurements that help you determine whether your experiment was a success.

In this article, we will briefly describe what each of the columns in this table means, and how they relate to your experiment.

The first two columns, **Metric name** and **Variant**, are straightforward. The first contains the names of the metrics included at the beginning of the experiment. The top metric is the primary metric; all other metrics are secondary metrics. The second contains the names of the variants in the experiment. This includes the control and all treatments.

**NOTE:** Click on a metric's name to view updated confidence interval over time and experiment visualization charts, as well as the bar chart or funnel for that metric.

**Significance** is the likelihood that the performance displayed for each test variant is actually different from zero, and is not due to random fluctuations in the data. The higher this value is, the more confident you can be in your results. More formally, this can be described as* 1 - p-value*.

**Relative performance** is the percent change in performance of the control that would be needed to match the given variant’s performance. In other words, it measures the difference between how the variant performed and how the control performed. (In other products, this is often called **relative lift**.) You can cross-check this value by expanding a single metric’s section and then dividing the absolute lift for a variant by the absolute value of the control for that metric. (The absolute lift is the value in parentheses in the **Absolute Performance** column.)

The specific meaning of the **absolute value** column depends on the metric type. For **unique** conversions, the value here will be expressed as a percentage, indicating the percentage of users (over the total number of exposed users) who converted for each variant.

**NOTE**: If you want to look at segments of users, from the *Analyze* tab click *Open in Analytics,* then add a *where* clause by clicking on *Select property... .* This will allow you to review results by a specific group of users.

Otherwise, the value indicates the **aggregate** (total events, sum of property value, average of property value) per exposed user. The denominator used here is the total number of exposures. For example, 10 total events / 4 exposures = on average, an exposed user had 2.5 conversion events.

The **confidence interval** column displays the confidence interval (the probability that a parameter will fall between a pair of values) of the **difference** between treatment and control. Mathematically, it can be expressed as `diff = metric_value(treatment) - metric_value(control)`

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You can understand this as a range of values that includes the parameter you’re trying to measure, which in this case is the difference in the **means** between the variant and the control. This is **not a probability**. Instead, you can interpret it this way: If we conduct this experiment 100 times and have our confidence level set at 95, we’d expect the true value of the parameter to fall within this range at least 95 times.

The confidence interval shown reveals characteristics about what the experiment has observed thus far:

- Confidence Interval
**contains**0: There’s not enough evidence to indicate there’s a difference between control and treatment. - Confidence Interval
**greater than**0: The interval (upper and lower confidence bounds) is greater than zero. Amplitude Experiment has accumulated enough observations to reach statistical significance, and you can conclude that the variant has a**positive effect**compared to control—for example, if you are looking at lift, a variant with a confidence interval greater than zero can be expected to perform better than the control. - Confidence Interval
**less than**0: Amplitude Experiment has accumulated enough observations to reach statistical significance, and you can conclude that the variant has a**negative effect**compared to control. If, as in the last example, you are looking at lift, a variant with a confidence interval less than zero can be expected to perform worse than the control.

If you have multiple variants, select the one you want to view in the confidence interval chart from the drop-down above the chart.