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Statistical significance (known as stat sig) measures how unusual your A/B test results would be if there were no difference between your variation and baseline and if the difference in lift was due to random chance alone.
When you observe a lift with 90% or higher statistical significance, the observed results are more unusual than expected in 90% of the cases if there was no lift. Assuming there was no difference in performance between the variation and the baseline, the higher your statistical significance, the more unusual your results would seem.
Optimizely does not declare a variation statistically significant or update the Confidence Interval until your experiment meets specific criteria for visitors and conversions. These criteria are different for experiments using numeric and binary metrics.
 Numeric metrics (such as revenue) – Do not require a specific number of conversions but require 100 visitors or sessions in the variations.
 Binary metrics – Require at least 100 visitors or sessions and 25 conversions in the variation and the baseline before a winner can be declared.
In statistics, you observe and use a population sample to infer the total population. Optimizely uses statistical significance to infer whether your variation caused movement in the metric.
Statistical significance helps Optimizely control the rate of errors in experiments. In any controlled experiment, there are three possible outcomes:

Accurate results – When there is an underlying, positive or negative difference between your original and your variation, the data shows a winner or loser. When there is not a difference, the data shows an inconclusive result.

Falsepositive (Type I Error) – Your test data shows a significant difference between your original and your variation, but there is random noise in the data—there is no underlying difference between your original and your variation.

Falsenegative (Type II Error) – Your test shows an inconclusive result, but your variation differs from your baseline.
Statistical significance measures how likely your improvement is from an actual change in underlying behavior instead of a false positive.
Lower significance levels may increase the likelihood of error but can also help you test more hypotheses and iterate faster. Higher significance levels decrease the error probability but require a larger sample.
Choosing the right significance level should balance the types of tests you are running, the confidence you want to have in the tests, and the amount of traffic you receive.
Onetailed and twotailed tests
When you run a test, you can run a onetailed or twotailed test.
 Twotailed tests – Detects the differences between the original and the variation in both directions. Twotailed tests tell you if your variation is a winner and if your variation is a loser.
 Onetailed test – Tells you whether the variation is a winner or a loser, but not both. Onetailed tests detect differences between the original and the variation in only one direction.
Optimizely uses twotailed tests because they are required for the false discovery rate control in Stats Engine. False discovery rate control is more important when making business decisions than whether you use a onetailed or twotailed test because you want to avoid implementing a false positive or negative.
Segmentation and statistical significance
You can segment your results to see if certain groups of visitors behave differently from your visitors overall. However, Optimizely does not perform additional false discovery rate correction for segmented results.
When repeatedly segmenting results and hunting for statistically significant results, significant results are much more likely to be false positives. You can limit the risk of false positives if you only test the most meaningful segments. The higher false discovery rate arises when you search for significant results among many segments.
Novelty effect and statistical significance
The statistical significance from a novelty effect stays for a long time. As the A/B test continues running, statistical significance calculations selfcorrect and consider how long the test is running for, not just the sample size.
Changing statistical significance setting
You should know certain tradeoffs associated with changing the statistical significance setting. In general,
 A higher significance setting is more accurate and increases the time required for Optimizely to declare significant results because it requires a larger sample size.
 A lower statistical significance level decreases the amount of time needed to declare significant results, but lowering the statistical significance setting also increases the chance that some results are false positives.
For information, see Change the statistical significance setting.
Changing your statistical significance setting instantly affects all currently running experiments. If your experiment has a goal with an 85% statistically significant winner, and you change your statistical significance setting from 90% to 80%, the next time you load your Experiment Results page, you see a winner (85% > 80%). Your difference intervals also shrink to reflect the reduced need for confidence.
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